ImageAnalysisForArcGIS10_2010

Image Analysis for ArcGIS®

Geographic Imaging by ERDAS

December 2010

Copyright © 2010 ERDAS, Inc.

All rights reserved.

Printed in the United States of America.

The information contained in this document is the exclusive property of ERDAS, Inc. This work is protected under United States copyright law and other international copyright treaties and conventions. No part of this work may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying and recording, or by any information storage or retrieval system, except as expressly permitted in writing by ERDAS, Inc. All requests should be sent to the attention of:

Manager, Technical DocumentationERDAS, Inc.5051 Peachtree Corners CircleSuite 100Norcross, GA 30092-2500 USA.

The information contained in this document is subject to change without notice.

Government Reserved Rights. MrSID technology incorporated in the Software was developed in part through a project at the Los Alamos National Laboratory, funded by the U.S. Government, managed under contract by the University of California (University), and is under exclusive commercial license to LizardTech, Inc. It is used under license from LizardTech. MrSID is protected by U.S. Patent No. 5,710,835. Foreign patents pending. The U.S. Government and the University have reserved rights in MrSID technology, including without limitation: (a) The U.S. Government has a non-exclusive, nontransferable, irrevocable, paid-up license to practice or have practiced throughout the world, for or on behalf of the United States, inventions covered by U.S. Patent No. 5,710,835 and has other rights under 35 U.S.C. § 200-212 and applicable implementing regulations; (b) If LizardTech's rights in the MrSID Technology terminate during the term of this Agreement, you may continue to use the Software. Any provisions of this license which could reasonably be deemed to do so would then protect the University and/or the U.S. Government; and (c) The University has no obligation to furnish any know-how, technical assistance, or technical data to users of MrSID software and makes no warranty or representation as to the validity of U.S. Patent 5,710,835 nor that the MrSID Software will not infringe any patent or other proprietary right. For further information about these provisions, contact LizardTech, 1008 Western Ave., Suite 200, Seattle, WA 98104.

ERDAS, ERDAS IMAGINE, Stereo Analyst, IMAGINE Essentials, IMAGINE Advantage, IMAGINE, Professional, IMAGINE VirtualGIS, Mapcomposer, Viewfinder, and Imagizer are registered trademarks of ERDAS, Inc.

Other companies and products mentioned herein are trademarks or registered trademarks of their respective owners.

vTable of Contents

Table of Contents v

Table of ContentsTable of Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vList of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .xiList of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xiiiForeword . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Introducing Image Analysis for ArcGIS . . . . . . . . . . . . . . . . . . . . . . . . . 3

Performing Tasks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Updating Databases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .4Categorizing Land Cover and Characterizing Sites . . . . . . . . . . . . . . . . . . . . . . . . . .5Identifying and Summarizing Natural Hazard Damage . . . . . . . . . . . . . . . . . . . . . . .6Identifying and Monitoring Urban Growth and Changes . . . . . . . . . . . . . . . . . . . . . .7Extracting Features Automatically . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .8Assessing Vegetation Stress . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9

Learning More . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Finding Answers to Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .9Getting Help on Your Computer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10Contacting ERDAS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10Contacting ESRI . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10ERDAS Education Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10ESRI Education Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .10

Quick-Start Tutorial . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Start Image Analysis for ArcGIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12Add the Image Analysis for ArcGIS Extension . . . . . . . . . . . . . . . . . . . . . . . . . . . . .12Add Toolbars . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .13

Exercise 2: Using Histogram Stretch . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14Add a Theme of Moscow . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14Apply a Histogram Equalization in the View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .15Apply a Histogram Equalization to modify a file . . . . . . . . . . . . . . . . . . . . . . . . . . . .16Apply an Invert Stretch to the Moscow Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . .17

Exercise 3: Identifying Similar Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19Add and Draw a Theme Depicting an Oil Spill . . . . . . . . . . . . . . . . . . . . . . . . . . . . .19Create a Shapefile . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .20Draw the Polygon with the Seed Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .22

Exercise 4: Finding Changed Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

vi Table of Contents

Find Changed Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24Add and Draw the Images of Atlanta . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .24Compute the Difference Due to Development . . . . . . . . . . . . . . . . . . . . . . . . . . . . .25Clear the View . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .27Use Thematic Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .27Add the Images of an Area Damaged by Hurricane . . . . . . . . . . . . . . . . . . . . . . . .28Create Three Classes of Land Cover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .29Assign Class Names and Colors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .31Categorize and Name the Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32Recode Class Names and Colors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .32Use Thematic Change to see Land Cover Changes . . . . . . . . . . . . . . . . . . . . . . . .34Add a Feature Theme that Shows the Property Boundary . . . . . . . . . . . . . . . . . . .35Make the Property Transparent . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .36Summarize the Area . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .37

Exercise 5: Mosaicking Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38Add and Draw the Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .38Zoom In to See Image Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .39Use Mosaic to Join the Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .41Create Custom Cutlines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .42

Exercise 6: Orthorectifying Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43Add Raster and Feature Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .43Select the Coordinate System for the Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .44Orthorectify your Image using GeoCorrection . . . . . . . . . . . . . . . . . . . . . . . . . . . . .45Place Fiducials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .47Place Links . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .48

What’s Next? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Applying Data Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51Seed Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Controlling the Seed Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .52Preparing to Use the Seed Tool . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .53Changing the Seed Radius . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .56

Image Info . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57NoData Value . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .57Using the Image Info Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .58

Options Dialog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59General Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .59Extent Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .60

Table of Contents vii

Cell Size Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .61Preferences Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .62Raster Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .65Using the Options Dialog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .66

Geoprocessing Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69Specifying Geoprocessing Options . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .69Updating Existing Geoprocessing Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .69

Using Data Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71Create New Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

Creating a New Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .73Subset Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

Subsetting an Image Spectrally . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .77Subsetting an Image Spatially . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .77

Mosaic Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78Mosaicking Images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .80

Reproject Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81Reprojecting an Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .81

Performing Spatial Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Applying convolution Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .84Convolution Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .84Convolution Formula . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .85Zero Sum Kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .86High-Frequency Kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .87Low-Frequency Kernels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .87Applying Convolution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .88

Non-Directional Edge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89Applying Non-Directional Edge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .91

Focal Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92Applying Focal Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .93

Resolution Merge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94Brovey Transform . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .94Applying Resolution Merge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .95

Using Radiometric Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97Contrast Stretch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .98Linear and Nonlinear . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .98

viii Table of Contents

Linear Contrast Stretch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .98Nonlinear Contrast Stretch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .98Piecewise Linear Contrast Stretch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .98Contrast Stretch on the Display . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .99Varying the Contrast Stretch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .99Applying a LUT Stretch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .100

Histogram Equalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101Effect on Contrast . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .104Performing Histogram Equalization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .104

Histogram Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105Performing Histogram Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .106

Brightness Inversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107Applying Brightness Inversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .109

Applying Spectral Enhancement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111RGB to IHS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

Converting RGB to IHS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .114IHS to RGB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115

Converting IHS to RGB . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .116Vegetative Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117

Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .117Examples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .117Image Algebra . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .118Applying Vegetative Indices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .119

Color IR to Natural Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120Changing Color IR to Natural Color . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .121

Performing GIS Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123Information Versus Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124Neighborhood Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 125

Performing Neighborhood Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .126Thematic Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127

Performing Thematic Change . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .128Summarize Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

Applying Summarize Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .130Recode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131

Recoding by Class Name . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .133

Table of Contents ix

Recoding by Symbology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .135Recoding with Previously Grouped Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .137

Using Utilities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139Image Difference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140

Using Image Difference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .142Layer Stack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

Using Layer Stack . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .144Rescale Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145

Using Rescale Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .146

Understanding Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147The Classification Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .148Unsupervised Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .148Supervised Training . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .148Signatures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .149Decision Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .149Parametric Decision Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .149Nonparametric Decision Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .149

Classification Tips . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 150Classification Scheme . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .150Supervised versus Unsupervised Classification . . . . . . . . . . . . . . . . . . . . . . . . . .151Classifying Enhanced Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .151Limiting Dimensions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .151

Unsupervised Classification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152Clusters . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .152ISODATA Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .152Initial cluster Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .153Pixel Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .153Percentage Unchanged . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .154Performing Unsupervised Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .155

Supervised Classification. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155Performing Supervised Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .156

Classification Decision Rules. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157Parametric Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .157Nonparametric Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .157Minimum Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .157

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Maximum Likelihood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .158Mahalanobis Distance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .159Parallelepiped . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .160

Using Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161Converting Raster to Features . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 162

Performing Raster to Features Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .164Performing Features to Raster Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .166

Applying GeoCorrection Tools . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167Disadvantages of Rectification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .169Georeferencing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .169Georeferencing Only . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .169Ground Control Points . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .169Entering GCP Coordinates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .170Tolerance of RMSE . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .170Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .170Thematic Files . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .171Orthorectification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .171

GeoCorrection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 171The GeoCorrection Properties Dialog . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .171General Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .171Links Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .172Elevation Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .174

SPOT. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176Panchromatic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .176XS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .177Stereoscopic Pairs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .177SPOT 4 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .178The Spot Properties Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .178

Polynomial Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 179Transformation Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .180Linear Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .180Nonlinear Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .181High-Order Polynomials . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .182Effects of Order . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .182Minimum Number of GCPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .187The Polynomial Properties Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .188

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Rubber Sheeting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189Triangulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .189Triangle-Based Rectification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .189Linear Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .189Nonlinear Transformation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .190Checkpoint Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .190

Camera . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191The Camera Properties Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .191Orientation Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .191Camera Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .192Fiducials Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .193

IKONOS, QuickBird, and RPC Properties . . . . . . . . . . . . . . . . . . . . . . . . 195IKONOS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .195QuickBird . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .196RPC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .196IKONOS, QuickBird, and RPC Parameters Tab . . . . . . . . . . . . . . . . . . . . . . . . . .197IKONOS, QuickBird, and RPC Chipping Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . .198Scale and Offset . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .199Arbitrary Affine . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .200

Landsat . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200Landsat 1-5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .200MSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .201TM . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .201Band Combinations for Displaying TM Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . .203Landsat 7 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .204Landsat 7 Data Types . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .204Landsat 7 Specifications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .204The Landsat Properties Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .205Parameters Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .205

Glossary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 207References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 231Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235

xiList of Figures

List of Figures xi

List of FiguresFigure 1: Airphoto with Shapefile of Streets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Figure 2: Classified Image for Radio Towers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5Figure 3: Before and After Hurricane Hugo, and Shapefile . . . . . . . . . . . . . . . . . . . 6Figure 4: Urban Areas Represented in Red . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7Figure 5: Oil Spill and Polygon Grown in Spill . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Figure 6: Crop Stress Shown through Vegetative Indices . . . . . . . . . . . . . . . . . . . . 9Figure 7: Seed Tool Properties Dialog Box . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52Figure 8: General Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59Figure 9: Extent Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Figure 10: Cell Size Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61Figure 11: Options – Preferences Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62Figure 12: Raster Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65Figure 13: Extent Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74Figure 14: Amazon TM Image before Spectral Subsetting . . . . . . . . . . . . . . . . . . 75Figure 15: Amazon TM Image after Spectral Subsetting . . . . . . . . . . . . . . . . . . . . 75Figure 16: Pentagon Image before Spatial Subsetting . . . . . . . . . . . . . . . . . . . . . 76Figure 17: Pentagon Subset Image after Analysis Extent . . . . . . . . . . . . . . . . . . . 76Figure 18: Convolution with High-Pass Filtering . . . . . . . . . . . . . . . . . . . . . . . . . . 88Figure 19: Image of Seattle before Non-Directional Edge . . . . . . . . . . . . . . . . . . . 90Figure 20: Image of Seattle after Non-Directional Edge . . . . . . . . . . . . . . . . . . . . 90Figure 21: Image before Focal Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92Figure 22: Image after Focal Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93Figure 23: High-Resolution, Multi-spectral, and Resolution Merge Images . . . . . . 96Figure 24: Contrast Stretch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 100Figure 25: Histogram Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Figure 26: Image before Brightness Inversion . . . . . . . . . . . . . . . . . . . . . . . . . . 108Figure 27: Image after Brightness Inversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108Figure 28: Variance of Intensity and Hue in RGB to IHS . . . . . . . . . . . . . . . . . . . 112Figure 29: Infrared Image of a Golf Course . . . . . . . . . . . . . . . . . . . . . . . . . . . . 120Figure 30: Natural Colors after Color IR to Natural Color . . . . . . . . . . . . . . . . . . 120Figure 31: Input Image from 1973 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127Figure 32: Input Image from 1994 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127Figure 33: Thematic Change Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128Figure 34: Image Showing Changes between 1973 and 1994 . . . . . . . . . . . . . . . 129Figure 35: Thematic Image before Recode by Class Name . . . . . . . . . . . . . . . . . 132Figure 36: Thematic Image after Recode by Class Name . . . . . . . . . . . . . . . . . . 132

xii List of Figures

Figure 37: Soil Data Image before Recode . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138Figure 38: Soil Data Image after Recode . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138Figure 39: Image Difference File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141Figure 40: Highlight Change File . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141Figure 41: Stacked Image . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Figure 42: Raster Image before Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163Figure 43: Raster Image after Conversion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163Figure 44: Elevation Source File Settings . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174Figure 45: Elevation Source Constant Settings . . . . . . . . . . . . . . . . . . . . . . . . . 174Figure 46: SPOT Panchromatic versus SPOT XS . . . . . . . . . . . . . . . . . . . . . . . . 177Figure 47: Orientation Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 191Figure 48: Camera Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192Figure 49: Fiducials Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193Figure 50: IKONOS, QuickBird, and RPC – Parameters Tab . . . . . . . . . . . . . . . . 197Figure 51: Chipping Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198Figure 52: Chipping Tab using Scale and Offset . . . . . . . . . . . . . . . . . . . . . . . . 199Figure 53: Chipping Tab using Arbitrary Affine . . . . . . . . . . . . . . . . . . . . . . . . . . 200Figure 54: Landsat MSS versus Landsat TM . . . . . . . . . . . . . . . . . . . . . . . . . . . 203Figure 55: Landsat Properties – Parameters Tab . . . . . . . . . . . . . . . . . . . . . . . . 205

xiiiList of Tables

List of Tables xiii

List of TablesTable 1: Bilinear Interpolation Resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63Table 2: Nearest Neighbor Resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64Table 3: Cubic Convolution Resampling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65Table 4: Data Type Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72Table 5: IR and R Bands of Common Sensors . . . . . . . . . . . . . . . . . . . . . . . . . . 118Table 6: SPOT XS Bands and Wavelengths . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177Table 7: SPOT 4 Bands and Wavelengths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 178Table 8: Number of GCPs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 187Table 9: IKONOS Bands and Wavelengths . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195Table 10: QuickBird Bands and Wavelengths . . . . . . . . . . . . . . . . . . . . . . . . . . . 196Table 11: TM Bands and Wavelengths . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202Table 12: Landsat 7 Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204

xiv List of Tables

1Foreword

Foreword 1

ForewordAn image of the Earth’s surface is a wealth of information. Images capture a permanent record of buildings, roads, rivers, trees, schools, mountains, and other features located on the Earth’s surface. But images go beyond simply recording features. Image serve the following purposes:

• Record relationships and processes as they occur in the real world.

• Are snapshots of geography, but they are also snapshots of reality.

• Chronicle our Earth and everything associated with it; they record a specific place at a specific point in time. They are snapshots of our changing cities, rivers, and mountains.

• Are snapshots of life on Earth.

The data in a geographic information system (GIS) needs to reflect reality, and snapshots of reality need to be incorporated and accurately transformed into instantaneously ready, easy-to-use information. From snapshots to digital reality, images are pivotal in creating and maintaining the information infrastructure used by today’s society. Geographic information systems today are carefully created with features, attributed behavior, analyzed relationships, and modeled processes. There are five essential questions that any GIS needs to answer: where, what, when, why, and how. Uncovering why, when, and how are all done within the GIS; images let you extract the where and what. Precisely where is that building? What is that parcel of land used for? What type of tree is that? The new extensions developed by ERDAS use imagery that let you accurately address the questions where and what, so you can then derive answers for the other three.But our Earth is changing! Urban growth, suburban sprawl, industrial usage and natural phenomena continually alter our geography. As our geography changes, so does the information we need to understand it. Because an image is a permanent record of features, behavior, relationships, and processes captured at a specific moment in time, using a series of images of the same area taken over time lets you more accurately model and analyze the relationships and processes that are important to our Earth.The extensions by ERDAS are technological breakthroughs that let you transform a snapshot of geography into information that digitally represents reality in the context of a GIS. Image Analysis™ for ArcGIS™ and Stereo Analyst® for ArcGIS are tools built on top of a GIS to maintain that GIS with up-to-date information. The extensions provided by ERDAS reliably transform imagery directly into your GIS for analyzing, mapping, visualizing, and understanding our world.

2 Foreword

On behalf of the Image Analysis for ArcGIS and Stereo Analyst for ArcGIS product teams, we wish you all the best in working with these products and hope you are successful in your GIS and mapping endeavors.

3Introducing Image Analysis for ArcGIS

Introducing Image Analysis for ArcGIS 3

Introducing Image Analysis for ArcGISImage Analysis for ArcGIS is primarily designed for natural resource and infrastructure management. This extension is useful in the fields of forestry, agriculture, environmental assessment, engineering, and infrastructure projects such as facility siting and corridor monitoring, and general geographic database update and maintenance.Today, imagery of the Earth’s surface is an integral part of desktop mapping and GIS, and it’s more important than ever to provide realistic backdrops to geographic databases and to quickly update details involving street or land use data.IN THIS CHAPTER

• Performing Tasks

• Learning More

4 Introducing Image Analysis for ArcGIS

Performing Tasks Image Analysis for ArcGIS lets you perform many tasks, including:

• Updating Databases

• Categorizing Land Cover and Characterizing Sites

• Identifying and Summarizing Natural Hazard Damage

• Identifying and Monitoring Urban Growth and Changes

• Extracting Features Automatically

• Assessing Vegetation Stress

Updating Databases There are many types of imagery to choose from in a wide range of scales, spatial and spectral resolutions, and map accuracies. Aerial photography is often the choice for map updating because of its high precision. With Image Analysis for ArcGIS you can use imagery to identify changes and make revisions and corrections to your geographic database.

Figure 1: Airphoto with Shapefile of Streets


Categorizing Land Cover and Characterizing Sites

Transmission towers for radio-based telecommunications must all be visible from each other, be within a certain range of elevations, and avoid fragile areas like wetlands. With Image Analysis for ArcGIS, you can categorize images into land cover classes to help identify suitable locations. You can use imagery and analysis techniques to identify wetlands and other environmentally sensitive areas.The classification features let you divide an image into many different classes, and then highlight them. In this case the areas not suitable for tower placement are highlighted, and the placement for the towers are sited appropriately.

Figure 2: Classified Image for Radio Towers


Identifying and Summarizing Natural Hazard Damage

When viewing a forest hit by a hurricane, you can use the mapping tools of Image Analysis for ArcGIS to show where the damage occurred. With other tools, you can show the condition of the vegetation, how much stress it suffers, and how much damage it sustained in the hurricane.Below, Landsat images taken before Hurricane Hugo in 1987 and after Hurricane Hugo in 1989, in conjunction with a shapefile that identifies the forest boundary, are used for comparison. Within the shapefile, you can see detailed tree stand inventory and management information.

Figure 3: Before and After Hurricane Hugo, and Shapefile


Identifying and Monitoring Urban Growth and Changes

Cities grow over time, and images give a good sense of how they grow and how to preserve remaining land by managing that growth. You can use Image Analysis for ArcGIS to reveal patterns of urban growth over time.Here, Landsat data spanning 21 years was analyzed for urban growth. The yellow urban areas from 1994 represent how much the city has grown beyond the red urban areas from 1973. The final view shows the differences in extent of urban land use and land cover between 1973 and 1994. Those differences are represented as classes.

Figure 4: Urban Areas Represented in Red


Extracting Features Automatically

Suppose you are responsible for mapping the extent of an oil spill as part of a rapid response effort. You can use synthetic aperture radar (SAR) data and Image Analysis for ArcGIS tools to identify and map the extent of such environmental hazards.The following image shows an oil spill off the northern coast of Spain. The first image shows the spill, and the second image shows a polygon grown in the oil spill using the Seed tool. The second image gives you an example of how you can isolate the exact extent of a particular pattern using Image Analysis for ArcGIS.

Figure 5: Oil Spill and Polygon Grown in Spill


Assessing Vegetation Stress

Crops experience different stresses throughout the growing season. You can use multispectral imagery and analysis tools to identify and monitor a crop’s health.In these images, the vegetative indices function is used to evaluate crop stress. The stressed areas are then automatically digitized and saved as a shapefile. You can use this type of information to help identify sources if there is variability in growth patterns, and then quickly update crop management plans.

Figure 6: Crop Stress Shown through Vegetative Indices

Learning More If you are just learning about GIS, you may want to read the following books about ArcCatalog and ArcMap™: Using ArcCatalog and Using ArcMap. Knowing about these applications can make your use of Image Analysis for ArcGIS much easier.See the quick-start tutorial in “Quick-Start Tutorial” on page 11 if you are ready to learn about how Image Analysis for ArcGIS works. In this tutorial, you learn how to adjust the appearance of an image, how to identify similar areas of an image, how to align an image to a feature theme, find areas of change, and mosaic images. The tutorial is written so that you can do the exercises using your computer and the example data supplied with Image Analysis for ArcGIS. If you’d rather, you can just read the tutorial to learn about the functionality of Image Analysis for ArcGIS.

Finding Answers to Questions

This book describes the typical workflow involved in creating and updating GIS data for mapping projects. The chapters are set up so that you first learn the theory behind certain applications, then you are introduced to the typical workflow you would apply to get the results you want. A glossary is provided to help you understand any terms you might not have seen before.


Getting Help on Your Computer

You can get a lot of information about the features of Image Analysis for ArcGIS by accessing the online help. To browse the online help contents, select Image Analysis desktop Help from the Image Analysis dropdown list. From this point you can use the table of contents, index, or search feature to locate the information you need. If you need online help for ArcGIS, click Help on the ArcMap toolbar and select ArcGIS Desktop Help.

Contacting ERDAS You can contact ERDAS for technical support, if needed, at 770-776-3650. Customers outside the United States should contact their local distributor. Visit ERDAS on the Web at www.erdas.com.

Contacting ESRI If you need to contact ESRI for technical support regarding ArcGIS, refer to “Getting technical support” in the Help system’s “Getting More Help” section. The telephone number for technical support is 909-793-3774. You can also visit ESRI on the Web at www.esri.com.

ERDAS Education Solutions

ERDAS offers instructor-based training for Image Analysis for ArcGIS. For more information, go to the training Web site at www.erdas.com. You can follow the training link to training centers, course schedules, and course registration.

ESRI Education Solutions

ESRI provides educational opportunities related to GIS, GIS applications, and technology. You can choose among instructor-led courses, Web-based courses, and self-study workbooks to find educational solutions that fit your learning style and budget. For more information, visit the Web site www.esri.com/education.

11Quick-Start Tutorial

Quick-Start Tutorial 11

Quick-Start TutorialNow that you know a little about the Image Analysis for ArcGIS extension and its potential applications, the following exercises give you hands-on experience in using many of the extension’s tools.In Image Analysis for ArcGIS, you can quickly identify areas with similar characteristics. This is useful in cases such as environmental disasters, burn areas, or oil spills. Once an area is defined, it can also be quickly saved into a shapefile, eliminating the need for manual digitizing.This tutorial shows you how to use some of the Image Analysis for ArcGIS tools and gives you a good introduction to using Image Analysis for ArcGIS for your own GIS needs.IN THIS CHAPTER

• Exercise 1: Getting Started

• Exercise 2: Using Histogram Stretch

• Exercise 3: Identifying Similar Areas

• Exercise 4: Finding Changed Areas

• Exercise 5: Mosaicking Images

• Exercise 6: Orthorectifying Images

• What’s Next?

12 Quick-Start Tutorial

Exercise 1: Getting Started

In this exercise, you learn how to start Image Analysis for ArcGIS and activate the toolbar associated with it. You gain access to all the important Image Analysis for ArcGIS features through its toolbar and menu list. After completing this exercise, you’ll be able to locate any Image Analysis for ArcGIS tool you need for preparation, enhancement, analysis, or geocorrection.This exercise assumes you have successfully installed Image Analysis for ArcGIS on your computer. You must use a single or dual monitor workstation that is configured for use with ArcMap and Image Analysis for ArcGIS. If you have not installed Image Analysis for ArcGIS, refer to the installation guide packaged on the CD and install it now.

Start Image Analysis for ArcGIS

To start Image Analysis for ArcGIS, follow this step:

1. Click the Start button on your desktop, point to All Programs, point to ArcGIS, and then click ArcMap to start the application.

Add the Image Analysis for ArcGIS Extension

To add the Image Analysis for ArcGIS extension, follow these steps:

1. When the ArcMap dialog opens, keep the option to create a new empty map and then click OK to open ArcMap.

2. Select Extensions from the Tools menu to open the Extensions dialog.

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3. Check the Image Analysis check box to add the extension to ArcMap.

Once the Image Analysis check box is enabled, the extension is activated.

4. Click Close.

Add Toolbars The Image Analysis toolbar is your gateway to many of the tools and features you can use with the extension. Use it to choose different analysis types, select a geocorrection type, or set links in an image, among other things.To add the Image Analysis toolbar, follow this step:

1. Click the Customize menu, point to Toolbars, and then select Image Analysis to add the Image Analysis toolbar.

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Exercise 2: Using Histogram Stretch

Image data, displayed without any contrast manipulation, might appear either too light or too dark, making it difficult to begin your analysis. Image Analysis for ArcGIS lets you display the same data in many different ways. For example, changing the distribution of pixels lets you change the brightness and contrast of the image. This is called histogram stretch, which lets you manipulate the display of data to make your image easier to visually interpret and evaluate.

Add a Theme of Moscow To add an Image Analysis for ArcGIS theme of Moscow, follow these steps:

1. Open a new view. If you are starting this exercise immediately after Exercise 1, you should have a new, empty view ready.

2. Click the Add Data button to open the Add Data dialog.

3. Select moscow_spot.tif. The path to the example data directory is:Program Files\ArcTutor\ImageAnalysis

4. Click Add to display the image in the view.The moscow_spot.tif image displays in the view.

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Apply a Histogram Equalization in the View

Standard deviations is the default histogram stretch applied to images by ArcGIS. You can apply histogram equalization to redistribute the data so that each display value has roughly the same number of data points. You can find more information about histogram equalization in “Using Radiometric Enhancement” on page 97.To apply a histogram equalization in the view, follow these steps:

1. Select moscow_spot.tif in the ArcMap table of contents.

2. Right-click and select Properties to open the Layer Properties dialog.

3. Click the Symbology tab.

4. Select RGB Composite in the Show box.

5. Check the order in the Band column and click the dropdown arrows to change any of the bands.Note: You can also change the order of the bands by clicking the color bar next to each band in the ArcMap table of contents. If you want bands to display in a certain order for each image that you draw in the view, click Tools, point to Options, and select Raster in ArcMap and change the default RGB band combinations.

6. Select Histogram Equalize from the Type dropdown list as the stretch type.

7. Click Apply and OK to close the Layer Properties dialog.

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Apply a Histogram Equalization to modify a file

You can apply the changes you made to a copy of the file permanently using Image Analysis for ArcGIS.To apply a histogram equalization to modify a file, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Radiometric Enhancement, and then select Histogram Equalization to open the Histogram Equalization dialog.

2. Select moscow_spot.tif from the Input Image dropdown list.

3. Leave the number in the Number of Bins field at 256 for this exercise. It defaults to 256 because the image is 8 bits. In the future, you can change this number to suit your needs.

4. Click the browse button for the Output Image field and navigate to the directory where you want your output images stored.

5. Type a name for your image, and then click Save. The path displays in the Output Image field.Note: You can access the Options dialog from the Image Analysis dropdown list, click the General tab, and then type the working directory you want to use. This step saves you time by automatically bringing up your working directory whenever you click the browse button to store an output image.

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6. Click OK to close the Histogram Equalization dialog. The equalized image displays in the ArcMap table of contents and in your view.This is the histogram equalized image of Moscow.

Apply an Invert Stretch to the Moscow Image

In this example, you apply an invert stretch to the image to redisplay it with its brightness values reversed. Areas that originally appeared bright are now dark, and dark areas are bright.To apply an invert stretch to the Moscow image, follow these steps:

1. Right-click the file that you equalized in the ArcMap table of contents and select Properties to go to the Symbology tab on the Layer Properties dialog.

2. Click the Histograms button in the Stretch box to view the histograms.

3. Click OK to return to the Symbology tab.

4. Check the Invert check box.

5. Click Apply and OK to display the inverted image.

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This is an inverted image of moscow_spot.tif.

You can apply different types of stretches to your image to emphasize different parts of the data. Depending on the original distribution of the data, one stretch might make the image display better than another. Image Analysis for ArcGIS lets you apply a LUT stretch, histogram equalization, or brightness inversion permanently to a copy of your files. It also provides a function that matches the histogram of one image to that of a reference image.The Layer Properties Symbology tab can be a learning tool to see the effect of stretches on the input and output histograms. You learn more about these stretches in “Using Radiometric Enhancement” on page 97.


Exercise 3: Identifying Similar Areas

With Image Analysis for ArcGIS, you can quickly identify areas with similar characteristics in images. This is useful for identifying environmental disasters or burn areas.Once an area is defined, you can save it into a shapefile. This lets you avoid the need for manual digitizing. To define the area, use the Seed tool to point to an area of interest such as a dark area on an image depicting an oil spill. The Seed tool returns a graphic polygon outlining areas with similar characteristics.

Add and Draw a Theme Depicting an Oil Spill

To add and draw an Image Analysis for ArcGIS theme depicting an oil spill, follow these steps:

1. If you are starting immediately after the previous exercise, clear your view by clicking the New Map File button on the ArcMap toolbar. You do not need to save the changes. If you are beginning here, start ArcMap and load the Image Analysis for ArcGIS extension.


3. Select radar_oilspill.img, and then click Add to display the image in your view.This is a radar image showing an oil spill off the northern coast of Spain.

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Create a Shapefile In this exercise, you use the Seed tool. The Seed tool grows a polygon graphic in the image that encompasses all similar and contiguous areas. However, you must first create a shapefile in ArcCatalog and start editing to activate the Seed tool. After going through these steps, click inside the area you want to highlight, in this case an oil spill, and create a polygon. The polygon lets you see how much area the oil spill covers.To create a shapefile using the Seed tool, follow these steps:

1. Click the Zoom In button on the Tools toolbar, and then drag a rectangle around the black area in the image to see the spill more clearly.

2. Click the ArcCatalog button. You can store the shapefile you create in the example data directory or navigate to a different directory.

3. Select the directory in the ArcCatalog table of contents, click File, point to New, and then select Shapefile to open the Create New ShapeFile dialog.

4. Type a name for the new shapefile oilspill in the Name field.

5. Select Polygon from the Feature Type dropdown list.

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6. Check the Show Details check box.

7. Click the Edit button to open the Spatial Reference Properties dialog.

8. Click the Import button to open the Browse for Dataset dialog, which contains the example data directory.

9. Select radar_oilspill.img, and then click Add to return to the Spatial Reference Properties dialog.

10. Click Apply and OK to return to the Create New Shapefile dialog.

11. Click OK to return to the ArcCatalog window.

12. Select the oilspill shapefile, and then drag it into the table of contents in the ArcMap window.

13. Close ArcCatalog.

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Draw the Polygon with the Seed Tool

To draw the polygon with the Seed tool, follow these steps:

1. Select Seed Tool Properties from the Image Analysis dropdown list to open the Seed Tool Properties dialog.

2. Type a seed radius of 10 in the Seed Radius field.Note: The seed radius is the number of pixels surrounding the target pixel. The range of values of those surrounding pixels is considered when the Seed tool grows the polygon.

3. Uncheck the Include Island Polygons check box.

4. Click OK to close the Seed Tool Properties dialog.

5. Click the Editor toolbar button on the ArcMap toolbar to display the Editor toolbar.

6. Select Start Editing from the Editor dropdown list.

7. Click the Seed Tool button, and then click a point in the center of the oil spill.

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Note: The Seed tool takes a few moments to produce the polygon.This is a polygon of an oil spill grown by the Seed tool.

Note: If you don’t automatically see the formed polygon in the image, click the refresh button at the bottom of the ArcMap window.You can see how the tool identifies the extent of the spill. An emergency team could be informed of the extent of this disaster to effectively plan a cleanup of the oil.


Exercise 4: Finding Changed Areas

The Image Analysis for ArcGIS extension lets you see changes over time. You can perform this type of analysis on either continuous data using image difference or thematic data using thematic change.

Find Changed Areas In the following example, you work with two continuous data images of the north metropolitan Atlanta, Georgia area—one from 1987 and one from 1992. Continuous data images are those obtained from remote sensors like Landsat and SPOT. This kind of data measures reflectance characteristics of the Earth’s surface, analogous to exposed film capturing an image. You can use image difference to identify areas that have been cleared of vegetation to construct a large regional shopping mall.

Add and Draw the Images of Atlanta

To add and draw the images of Atlanta, follow these steps:

1. If you are starting immediately after the previous exercise, clear your view by clicking the New Map File button on the ArcMap toolbar. You do not need to save the changes. If you are beginning here, start ArcMap and load the Image Analysis for ArcGIS extension.


3. Hold down the Ctrl key, and then select both atl_spotp_87.img and atl_spotp_92.img.

4. Click Add to display the images in your view.With images active in the view, you can calculate the difference between them.


Compute the Difference Due to Development

In this exercise, you learn how to use image difference, which is useful for analyzing images of the same area to identify any changes in land cover features over time. Image difference performs a subtraction of one theme from another. This change is highlighted in green and red masks that depict increasing and decreasing values.To compute the difference due to development, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Utilities, and then select Image Difference to open the Image Difference dialog.

2. Click the browse button for the Before Theme field and navigate to the file named atl_spotp_87.img.

3. Select Layer_1 from the Before Layer dropdown list.

4. Click the browse button for the After Image field and navigate to the file named atl_spotp_92.img.

5. Select Layer_1 from the After Layer dropdown list.

6. Click the As Percent button in the Highlight Changes box.

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7. Type 15 in the Increases More Than field.

8. Type 15 in the Decreases More Than field.

9. Navigate to the directory where you want your difference image file stored, type the name of the file, and then click Save.

10. Navigate to the directory where you want your highlight image file stored, type the name of the file, and then click Save.

11. Click OK to close the Image Difference dialog.The highlight image and difference image files display in the ArcMap table of contents and in the view.The image difference image shows the results of the subtraction of the before theme from the after theme.


12. Uncheck the Difference Image check box in the ArcMap table of contents to disable it and display the highlight image.

The image difference function calculates the difference in pixel values with the 15 percent parameter you set. It finds areas with at least a 15 percent increase in designated clearing and highlights them in green.Image difference also finds areas that have less than a 15 percent decrease than before (designating an area that has increased vegetation or an area that was once dry, but is now wet) and highlights them in red. These changes display in the highlight change file.

Clear the View You can now clear the view and either go to the next portion of this exercise, Exercise 4: Finding Changed Areas on page 27, or end the session by closing ArcMap. If you want to shut down ArcMap with Image Analysis for ArcGIS, click the File menu and select Exit. Click No when asked to save changes.

Use Thematic Change Image Analysis for ArcGIS provides the thematic change feature to make comparisons between thematic data images. Thematic change creates a theme that shows all possible combinations of change to display an area’s land cover class change over time.Thematic change is similar to image difference in that it computes changes between the same area at different points in time. However, thematic change can only be used with thematic data (data that is classified into distinct categories). An example of thematic data is a vegetation class map.The next example uses two images of an area near Hagan Landing, South Carolina. The images were taken in 1987 and 1989, before and after Hurricane Hugo. Suppose you are the forest manager for a paper company that owns a parcel of land in the hurricane’s path. With Image Analysis for ArcGIS, you can see exactly how much of your forested land was destroyed by the storm.


Add the Images of an Area Damaged by Hurricane

To add the images of an area damaged by Hurricane Hugo, follow these steps:

1. If you are starting immediately after the previous exercise, clear your view by clicking the New Map File button on your ArcMap toolbar. You do not need to save the changes. If you are beginning here, start ArcMap and load the Image Analysis for ArcGIS extension.


3. Hold down the Ctrl key, and select both tm_oct87.img and tm_oct89.img.

4. Click Add to display the images in your view.This image shows an area damaged by Hurricane Hugo.


Create Three Classes of Land Cover

Before you calculate thematic change, you must first categorize the before and after themes. You can access the categorize function through unsupervised classification, which is an option available on the Image Analysis dropdown list. You use the thematic themes created from those classifications to complete the thematic change calculation.To create three classes of land cover, follow these steps:

1. Select tm_oct87.img from the Layers dropdown list on the Image Analysis toolbar.

2. Click the Image Analysis dropdown arrow, point to Classification, and then select Unsupervised/Categorize to open the Unsupervised Classification dialog.

3. Click the browse button for the Input Image field and navigate to the directory with the tm_oct87.img file.

4. Type 3 in the Desired Number of Classes field.

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5. Navigate to the directory where you want to store the output image, type the file name (for this example, use unsupervised_class_87), and then click Save.

6. Click OK to close the Unsupervised Classification dialog.Note: Using unsupervised classification to categorize continuous images into thematic classes is useful when you are unfamiliar with the data that makes up your image. When you designate the number of classes you want the data divided into, Image Analysis for ArcGIS performs a calculation assigning pixels to classes depending on their values. By using unsupervised classification, you are better able to quantify areas of different land cover in your image. You can then assign the classes names like water, forest, and bare soil.

7. Uncheck the tm_oct87.img check box in the ArcMap table of contents so the original theme is not drawn in the view. This step also makes the remaining themes draw faster.

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Assign Class Names and Colors

To give the classes names and assign colors to represent them, follow these steps:

1. Double-click unsupervised_class_87.img in the ArcMap table of contents to open the Layer Properties dialog.


3. Verify that Class_names is selected in the Value Field field.

4. Select Class 001 in the Label column, and then type Water.

5. Double-click the color bar in the Symbol column for Class 001, and select blue from the color palette.

6. Select Class 002 in the Label column, and then type Forest.

7. Double-click the color bar in the Symbol column for Class 002, and select green from the color palette.

8. Select Class 003 in the Label column, and then type Bare Soil.

9. Double-click the color bar in the Symbol column for Class 003, and select a tan or light brown color from the color palette.

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Categorize and Name the Areas

To categorize and name the areas in the post-hurricane image, follow these steps:

1. Follow the steps provided for the theme tm_oct87.img in Exercise 4: Finding Changed Areas on page 29 and Exercise 4: Finding Changed Areas on page 31 to categorize the classes of the tm_oct89.img theme.

2. Uncheck the tm_oct89.img check box in the ArcMap table of contents so that it does not draw in the view.

Recode Class Names and Colors

After modifying the class names and colors using the Unique Values page on the Symbology tab on the Layers Properties dialog, you can permanently save these changes. Using recode with the From View option, the class names and colors are saved to a thematic image file.


To recode class names and colors permanently to a file, follow these steps:

1. Click the Image Analysis dropdown arrow, point to GIS Analysis, and then select Recode to open the Recode dialog.

2. Click the browse button for the Input Image field and select one of the classified images.

3. Select From View from the Map Pixel Value through Field dropdown list.

4. Type the name of the output image in the Output Image field, or click the browse button, navigate to your working directory, name the output image, and then click Save.

5. Click OK to close the Recode dialog.Now use the same steps to perform a recode on the other classified image of the Hugo area so that both images have your class names and colors permanently saved.

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Use Thematic Change to see Land Cover Changes

To use thematic change to see how land cover changed because of Hugo, follow these steps:

1. Make sure the check boxes for both images you recoded are checked in the ArcMap table of contents so they are active in the view.

2. Click the Image Analysis dropdown arrow, point to GIS Analysis, and then select Thematic Change to open the Thematic Change dialog.

3. Select the 87 recoded classification image from the Before Theme dropdown list.

4. Select the 89 recoded classification image from the After Theme dropdown list.

5. Navigate to the directory where you want to store the Output Image, type the file name, and then click Save.

6. Click OK to close the Thematic Change dialog.

7. Check the Thematic Change check box in the ArcMap table of contents to draw it in the view.

8. Double-click the Thematic Change title to open the Layer Properties dialog.

9. Double-click the color bar in the ArcMap table of contents for Was: Forest, is now: Bare Soil.

10. Click the color red in the color palette, and then click OK. If you don’t want to choose red, you can use any color you like.

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You can see the amount of destruction in red. The red shows what was forest and is now bare soil.

Add a Feature Theme that Shows the Property Boundary

Using thematic change, the overall damage caused by the hurricane is clear. Next, you see how much damage actually occurred on the paper company’s land.To add a feature theme that shows the property boundary, follow these steps:


2. Select property.shp, and then click Add.This figure displays a thematic change image with the property shapefile.

Make the Property Transparent

To make the property transparent, follow these steps:


1. Double-click the property theme in the ArcMap table of contents to open the Layer Properties dialog.

2. Click the Symbology tab, and then click the color symbol to open the Symbol Selector dialog.

3. Click the Hollow symbol in the box on the left side of the Symbol Selector dialog.

4. Type 3 in the Outline Width field.

5. Select a color that easily stands out to show your property line from the Outline Color dropdown list.

6. Click OK to close the Symbol Selector dialog.

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7. Click Apply and OK when you return to the Symbology tab to close the Layer Properties dialog.The yellow outline clearly shows the devastation within the paper company’s property boundaries.

Summarize the Area Next, you use the summarize areas function to give area calculations of loss inside the polygon you created.To summarize the area, follow these steps:

1. Click the Image Analysis dropdown arrow, point to GIS Analysis, and then select Summarize Areas to open the Summarize Areas dialog.

2. Select a file from the Zone Theme dropdown list, or navigate to the directory where it is stored.

3. Select an attribute from the Zone Attribute dropdown list.

4. Select a file from the Class Theme dropdown list, or navigate to the directory where it is stored.

5. Click the browse button for the Summarize Results Table field to specify a name for the new summarize areas table that is created.

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6. Click OK to summarize areas.

7. View the different results of the summarized areas in the Summarize Areas Results dialog that displays.

8. Click Close to close the Summarize Areas Results dialog, or click Export to Table to export the information to a text file.When the process completes, the resulting table is added to ArcMap. Click the Source tab in the ArcMap table of contents to see the new table.

Exercise 5: Mosaicking Images

Image Analysis for ArcGIS lets you mosaic multiple images. When you mosaic images, you join them together to form one image that covers the entire area. In the following exercise, you mosaic two air photos with the same resolution.

Add and Draw the Images To add and draw the mosaicked images, follow these steps:

1. Clear your view by clicking the New Map File button on the ArcMap toolbar if you are starting immediately after the previous exercise. You do not need to save the changes. If you are beginning here, start ArcMap and load the Image Analysis for ArcGIS extension with a new map.


3. Hold down the Ctrl key and select both Airphoto1.img and Airphoto2.img.

4. Click Add to display the images in your view.


5. Click Airphoto1.img and drag it so that it is at the top of the ArcMap table of contents.The Mosaic tool joins the two air photos as they display in the view. Whichever image is on top is also on top in the mosaicked image.

Zoom In to See Image Details

To zoom in to see image details, follow these steps:

1. Right-click Airphoto1.img in the ArcMap table of contents and select Zoom to Raster Resolution.The two images display at a 1:1 resolution. You can now use the Pan tool to see how they overlap.


2. Click the Pan button on the Tool toolbar, and then maneuver the images in the view.

This illustration shows where the two images overlap.

3. Click the Full Extent button so that both images display their entirety in the view.

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Use Mosaic to Join the Images

To use Mosaic to join the two images, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Data Preparation, and then select Mosaic Images to open the Mosaic Images dialog.

2. Select Use Order Displayed from the Handle Images Overlaps By dropdown list.

3. Check the Automatically Crop Images By check box if you want to automatically crop your images, and then type the percentage by which to crop the images in the Percent field.

4. Click the Brightness/Contrast button as the Color Balance By setting.

5. Navigate to the directory where you want to save your files, type the file name, and then click Save.

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6. Click OK to return to the Mosaic Images dialog, and then click OK to close.The Mosaic function joins the two images as they display in the view. Airphoto1 is mosaicked over Airphoto2.

Create Custom Cutlines It is possible to create custom cutlines for your images. You must subset the images prior to mosaicking to do so.Note: This exercise is optional.To create custom cutlines, follow these steps:

1. Add the image you want to subset to your view.

2. Add an existing shapefile, or create a new polygon shapefile and digitize the desired extent of your output file.

3. Click the Image Analysis dropdown arrow, point to Data Preparation, and then select Subset Image to open the Subset Image dialog.

4. Click the browse button for the Input Image field and navigate to where your polygon shapefile is stored.

5. Type the name of the output image in the Output Image field, or navigate to the directory where you want it stored.

6. Click OK to subset the image.

7. Repeat for all of the images you want to create cutlines for.

8. Follow the steps in Exercise 5: Mosaicking Images on page 41 to mosaic the images.


Exercise 6: Orthorectifying Images

The Image Analysis for ArcGIS extension for ArcGIS has a feature called GeoCorrection properties. The function of this feature is to rectify images. One of the tools that makes up GeoCorrection properties is the camera model.In this exercise you orthorectify images using the camera model in GeoCorrection properties.

Add Raster and Feature Datasets

To add raster and feature datasets, follow these steps:

1. Clear your view by clicking the New Map File button on the ArcMap toolbar if you are starting immediately after the previous exercise. You do not need to save the changes. If you are beginning here, start ArcMap and load the Image Analysis for ArcGIS extension with a new map.


3. Hold down the Ctrl key and select both ps_napp.img and ps_streets.shp.

4. Click Add to display the images in your view.

5. Right-click ps_napp.img in the ArcMap table of contents and select Zoom to Layer.The image is drawn in the view. You can see the fiducial markings around the edges and at the top.

Select the Coordinate System for the Image

This procedure defines the coordinate system for the data frame in Image Analysis for ArcGIS.


To select the coordinate system for the image, follow these steps:

1. Right-click the image in your view and select Data Frame Properties to open the Data Frame Properties dialog.

2. Click the Coordinate System tab.

3. Select Predefined in the Select a Coordinate System box.

4. Click Projected Coordinate Systems, and then click Utm.

5. Click NAD 1927, and then click NAD 1927 UTM Zone 11N.

6. Click Apply and OK to close the Data Frame Properties dialog.

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Orthorectify your Image using GeoCorrection

To orthorectify your image using GeoCorrection properties, follow these steps:

1. Select Camera from the Model Types dropdown list on the Image Analysis toolbar.

2. Click the GeoCorrection Properties button to open the Camera Properties dialog.

3. Click the Elevation tab.

4. Click File to use as the elevation source.

5. Navigate to the ArcGIS ArcTutor directory, and select ps_dem.img as the elevation file.

6. Select Meters from the Elevation Units dropdown list.

7. Check the Account for Earth’s Curvature check box.

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8. Click the Camera tab.

9. Select Default Wild from the Camera Name dropdown list.

10. Type -0.004 in the X field and 0.000 in the Y field in the Principal Point box.

11. Type 152.804 in the Focal Length field.

12. Type 4 in the Number of Fiducials field, and then press the Tab key to update the fiducials table below this field.

13. Type the following coordinates in the table cells. Press the Tab key to move from cell to cell.

14. Type the camera name in the Camera Name field.

15. Click Save to navigate to a directory and save the camera information with the camera name.

16. Click Save to return to the Camera tab.

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1. -106.000 106.000

2. 105.999 105.9942

3. 105.998 -105.999

4. -106.008 -105.999


17. Click Apply and move to the next section.

Place Fiducials To place fiducials, follow these steps:

1. Click the Fiducials tab.

2. Make sure the first fiducial orientation is selected.

3. Click the Fiducial Measurement button. Image Analysis for ArcGIS takes you to the approximate location of the first fiducial placement, and your cursor becomes a crosshair.

4. Click the Fixed Zoom In button on the Tools toolbar.

5. Zoom in until you see the fiducial, and then click the crosshair. Image Analysis for ArcGIS takes you to each of the four points where you can click the crosshair in the fiducial marker.

6. Right-click the image in the ArcMap table of contents and select Zoom to Layer after you finish placing fiducials.You see that both the image and the shapefile display in the view.

7. View the root mean square error (RMSE) on the Fiducials tab by reopening the Camera Properties dialog. The RMSE should be less than 1.

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8. Click OK to close the Camera Properties dialog.

After placing fiducials, both the image and the shapefile are shown in the view for rectification.

Place Links To place links, follow these steps:

1. Click the Add Links button.

2. Looking closely at the image and shapefile in the view, and using the next image as a guide, line up where to place the first link. Follow the markers in the next image to place the first three links. You must click the crosshair on the point in the image first and then click the corresponding location in the shapefile.

Your first link should look approximately like this:

3. Place links 2 and 3.


After placing the third link, your image should look something like this:

4. Zoom to the lower-left corner of the image, and place a link according to the previous image.Your image should warp and become aligned with the streets shapefile. You can use the Zoom tool to draw a rectangle around the aligned area and zoom in to see it more clearly.

Now take a look at the Total RMSE field on the Links tab on the Camera Properties dialog. Your RSME should be less than 1. If the error is higher than 1, you might need to redo the point selection. Remove the point first by clicking it, and then pressing the Delete key.

5. Select Save As from the Image Analysis dropdown list to save the image.


What’s Next? This tutorial introduced you to some features and basic functions of Image Analysis for ArcGIS. The following chapters go into greater detail about the different tools and elements of Image Analysis for ArcGIS, and include instructions on how to use them to your advantage.

51Applying Data Tools

Applying Data Tools 51

Applying Data ToolsThere are three options in the Image Analysis dropdown list. All three aid you in manipulating, analyzing, and altering your data so you can produce results that are easier to interpret. The options are as follows:

• Seed Tool Properties – Automatically generates feature layer polygons of similar spectral value.

• Image Info – Gives you the ability to apply a NoData value and recalculate statistics.

• Options – Lets you change extent, cell size, preferences, and more.

IN THIS CHAPTER

• Seed Tool

• Image Info

• Options Dialog

• Geoprocessing Tools

52 Applying Data Tools

Seed Tool The main function of the Seed tool is to automatically generate feature layer polygons of similar spectral value. Before using the Seed tool, you must first create the shapefile for the image you are using in ArcCatalog. To do so, open ArcCatalog, create a new shapefile in the directory you want to use, name it, select Polygon as the shapefile type, and then select Start Editing from the Editor dropdown list.After clicking the Seed Tool button on the Image Analysis toolbar, you can either click in an image on a single point, or you can drag a rectangle in a portion of the image that interests you. You can experiment with which method works best with your data. When you finish growing the polygon, select Stop Editing from the Editor dropdown list.Any bands used in growing the polygon are controlled by the current visible bands set in the Layer Properties dialog. If you display only one band, such as the red band when interested in vegetation analysis, the Seed tool only looks at the statistics of that band to create the polygon. If you display all the bands (red, green, and blue), the Seed tool evaluates the statistics in each band of data before creating the polygon.When a polygon is created using the Seed tool, it is added to the shapefile. Like other ArcGIS graphics, you can change the appearance of the polygon produced by the Seed tool using the graphics tools.

Controlling the Seed Tool You use the Seed tool by clicking it on the Image Analysis toolbar, and then clicking an image after generating a shapefile. The defaults usually produce a good result. However, if you want more control over the parameters of the Seed tool, you can use the Seed Tool Properties dialog by selecting it from the Image Analysis dropdown list.

Figure 7: Seed Tool Properties Dialog Box


Seed RadiusWhen you use the simple click method, the Seed tool is controlled by the seed radius. You can change the number of pixels of the seed radius using the Seed Radius field in the Seed Tool Properties dialog. The Image Analysis for ArcGIS default seed radius is 5 pixels.The seed radius determines how selective the Seed tool is when selecting contiguous pixels. A larger seed radius includes more pixels to calculate the range of pixel values used to grow the polygon, and typically produces a larger polygon. A smaller seed radius uses fewer pixels to determine the range. Setting the seed radius to 0.5 or less restricts the polygon to growing over pixels with the exact value as the pixel you click in the image. This is useful for thematic images in which a contiguous area has a single pixel value instead of a range of values like continuous data.

Island PolygonsThe other option in the Seed Tool Properties dialog is Include Island Polygons. Leave this option checked if you want to include small, non-contiguous polygons. You can turn it off for single feature mapping where you want to see a more refined boundary.

Preparing to Use the Seed Tool

To activate the Seed tool and generate a polygon in your image, follow these steps:

1. Click the ArcCatalog button on the Standard toolbar to open ArcCatalog, and then make sure your working directory displays in the window.

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2. Click File, point to New, and then select Shapefile to open the Create New Shapefile dialog.

3. Type a name for the new shapefile in the Name field.

4. Select Polygon from the Feature Type dropdown list.

5. Check the Show Details check box.

6. Click Edit to open the Spatial Reference Properties dialog.

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7. Click either the Select, Import, or New button and enter the coordinate system for the new shapefile to use. Clicking Import lets you import the coordinates of the image you are creating the shapefile for.

8. Click Apply and OK to close the Spatial Reference Properties dialog.

9. Click OK to close the Create New Shapefile dialog.

10. Drag the new shapefile from the ArcCatalog table of contents into the ArcMap table of contents.

11. Close ArcCatalog.

12. Select Start Editing from the Editor dropdown list on the Editor toolbar.

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Changing the Seed Radius

To change the seed radius and include island polygons, follow these steps:

1. Select Seed Tool Properties from the Image Analysis dropdown list to open the Seed Tool Properties dialog.

2. Type a new value in the Seed Radius field.

3. Check the Include Island Polygons check box if you want to activate this option.

4. Click OK to close the Seed Radius dialog and growing the polygon over the image with the Seed tool.

5. Select Stop Editing from the Editor dropdown list on the Editor toolbar.

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Image Info When analyzing images, you often have pixel values you need to alter or manipulate to perceive different parts of the image better. The Image Info feature of Image Analysis for ArcGIS lets you choose a NoData value for your image so that a pixel value that is unimportant in your image can be designated as such and is excluded from processes like statistics calculations.You can find the Image Info dialog on the Image Analysis dropdown list. When you open this dialog, the images in your view display in the Layer Selection dropdown list. You can use the Image Info dialog as follows:

• Type a value in the NoData Value field to set the NoData pixels in your image.

• Apply NoData to a single layer of your image instead of the entire image. When you apply NoData to a single layer, it is important that you click the Apply button in the Image Info dialog before moving to the next layer. For more information, see the “NoData Value” section that follows.

• Recalculate statistics (Recalc Stats) for single bands by clicking the Current Band button in the Statistics box. Please remember that if you click Recalc Stats while Current Band is selected, Image Info only recalculates the statistics for that band. If you want to set NoData for a single band, but recalculate statistics for all bands, click the All Bands button after specifying NoData in the single bands, and then recalculate for all.

• You can close and refresh the ArcMap display to see the NoData value applied by clicking the refresh button at the bottom of the ArcMap window.

• Manually override the setting in the Representation Type box. Image Analysis for ArcGIS automatically chooses continuous or thematic depending on the type of image in your view.

NoData Value The NoData Value section of the Image Info dialog lets you label certain areas of your image as NoData when the pixel values in that area are not important to your statistics or image. To do so, assign a value that no other pixel in the image has to the pixels you want to classify as NoData. Using 0 is not always recommended because 0 can be a valid value in your image. Look at the Minimum and Maximum values in the Statistics box and choose a NoData value that is any number between these minimum and maximum values. You can type N/A or leave the area blank so that you have no NoData assigned if you don't want to use this option.


Sometimes the pixel value you choose as NoData is already in use, causing NoData to match some other part of your image. This problem becomes evident when the image displays in the view and there are black spots or triangles where it should be clear, or clear spots where it should be black.

Using the Image Info Dialog Box

To use the Image Info dialog, follow these steps:

1. Display your image in the view.

2. Select Image Info from the Image Analysis dropdown list to open the Image Info dialog.

3. Click the Layer Selection dropdown arrow to make sure the correct image is displayed.

4. Click either the All Bands or Current Band button.

5. Click the Statistics dropdown arrow to make sure the layer you want to recalculate is selected if you clicked the Current Band button.

6. Type a value in the NoData Value field, or type N/A if you don’t want to assign a pixel to the NoData value.

7. Make sure the correct representation type is chosen for your image.

8. Click the Recalc Stats button to recalculate the statistics using the NoData value.

9. Click Apply and OK to close the Image Info dialog.

10. Click the refresh button at the bottom of the ArcMap window to refresh the display.

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Options Dialog You can access the Options dialog using the Image Analysis dropdown list. This dialog lets you set an analysis mask as well as the extent, cell size, preferences, and raster for a single operation or future operations. These options default to process appropriate options, but you can change them if necessary. You can use the Options dialog with any Image Analysis feature, but it is particularly useful with the Data Preparation features that are covered in “Using Data Preparation” on page 71.Note: You can specify any of the settings in the Options dialog from the Environment Settings dialog, which can be displayed by clicking the Environments button at the bottom of any process dialog. You can also access this dialog by clicking Tools/Options/Geoprocessing tab, and then clicking the Environments button.The Options dialog has five tabs: General Tab, Extent Tab, Cell Size Tab, Preferences Tab, and Raster Tab.

General Tab On the General tab, your output directory displays and the analysis mask defaults to none. However, if you click the Analysis Mask dropdown arrow, you can set it to any file.

Figure 8: General Tab

You can store your output images and shapefiles in one working directory by navigating to that directory or typing the directory name in the Working Directory field. This allows your working directory to automatically come up every time you click the browse button for an output image. The Analysis Coordinate System option lets you choose which coordinate system to save the image with—the one for the input, the one for the active data frame, or the one you specify.


Extent Tab The Extent tab lets you control how much of a theme you want to use during processing. You can do this by setting the analysis extent.

Figure 9: Extent Tab

All of the settings on the Extent tab become active when you select any of the following extents from the Analysis Extent dropdown list:

• Same as Display – Refers to the area currently displayed in the view. If the view is zoomed in on a portion of a theme, the functions only operate on that portion of the theme.

• Same as Layer – Lets you set the extent of processing to the same extent of another layer in your table of contents. You can also click the browse button to select a dataset to use as the analysis extent. If you click this button, you can navigate to the directory where your data is stored and select a file.

• As Specified Below – Lets you fill in the information for the extent.

When you select an extent that activates the rest of the Extent tab, the fields are Top, Right, Bottom, and Left. If you are familiar with the data and want to enter exact coordinates, you can do so in these fields.Same as Display and As Specified Below also activate the Snap Extent To dropdown list, allowing you to select an image to snap the analysis mask to.


The other settings on the Analysis extent dropdown list are:

• Intersection of Inputs – The default extent for all functions except Mosaic. When this extent is set, Image Analysis for ArcGIS performs functions on the area of overlap common to the input images. Portions of the images outside the area of overlap are discounted from analysis.

• Union of Inputs – The default setting of analysis extent for mosaicking. When this extent is set, Image Analysis for ArcGIS uses the union of every input theme. It is recommended that you keep this default setting when mosaicking images. If you change it, you must check the Use Extent from Analysis Options check box in the Mosaic Images dialog.

• Use Function Defaults – The extent that lets you use the general processing defaults for the specific function or any settings you set in the Environment Settings dialog.

Cell Size Tab The third tab on the Options dialog is Cell Size. This is for the output cell size of images you produce using Image Analysis for ArcGIS.

Figure 10: Cell Size Tab

The first field on the Cell Size tab is the Analysis Cell Size dropdown list. The options in this list are as follows:

• Maximum of Inputs – Yields an output that has the maximum resolution of the input files. For example, if you use Image Difference on a 10 m image and a 20 m image, the output is a 20 m image.


• Minimum of Inputs – Produces an output that has the minimum resolution of the input files. For example, if you use Image Difference on a 10 m image and a 20 m image, the output is a 10 m image.

• As Specified Below – Lets you enter the cell size you want, and Image Analysis for ArcGIS adjusts the output accordingly.

• Same as Layer "...." – Indicates a layer in the view, and the Cell Size field reflects the current cell size of that layer.

The cell size displays in either meters or feet. You can change the cell size by selecting Data Frame Properties from the View menu in ArcMap to open the Data Frame Properties dialog. Click the General tab, and then select either Meters or Feet from the Map dropdown list in the Units box.You should not manually update the Number of Rows and Number of Columns fields on the Cell Size tab because they automatically update as analysis properties are changed.

Preferences Tab It is recommended that you leave the preference to the default of Bilinear Interpolation. However, you can change it to Nearest Neighbor or Cubic Convolution if your data requires it.

Figure 11: Options – Preferences Tab


The Resample Using settings on the Preferences tab are defined as follows:

• Bilinear Interpolation – A resampling method that uses the data file values of four pixels in a 2 × 2 window to calculate an output data file value. It does this by computing a weighted average of the input data file values with a bilinear function.

• Nearest Neighbor – A resampling method in which the output data file value is equal to the input pixel that has coordinates closest to the retransformed coordinates of the output pixel.

• Cubic Convolution – A resampling method that uses the data file values of 16 pixels in a 4 × 4 window to calculate an output data file value with a cubic function.

This table lists the advantages and disadvantages of bilinear interpolation resampling.

Table 1: Bilinear Interpolation Resampling

Advantages Disadvantages

Results in output images that are smoother, without the stair-stepped effect that is possible with Nearest Neighbor.

Has the effect of a low-frequency convolution because pixels are averaged. Also, edges are smoothed, and some extremes of the data file values are lost.

More spatially accurate than Nearest Neighbor.

Often used when changing the cell size of the data, such as in SPOT/TM merges within the 2 x 2 resampling matrix limit.


This table lists the advantages and disadvantages of nearest neighbor resampling.

Table 2: Nearest Neighbor Resampling


Transfers original data values without averaging them as the other methods do; therefore, the extremes and subtleties of the data values are not lost. This is an important consideration when discriminating between vegetation types, locating an edge associated with a lineament, or determining different levels of turbidity or temperatures in a lake (Jensen 1996).

Usually results in a stair-stepped effect around diagonal lines and curves when used to resample from a larger to a smaller grid size.

Suitable for use before classification. Can drop data values, while duplicating other values.

Easiest of the three methods to compute, and the fastest to use.

Can result in breaks or gaps in a network of linear data when used on linear thematic data (for example, roads or streams). Appropriate for thematic files, which

can have data file values based on a qualitative (nominal or ordinal) or quantitative (interval or ratio) system. The averaging performed with bilinear interpolation and cubic convolution is not suited to a qualitative class value system.


This table lists the advantages and disadvantages of cubic convolution resampling.

Table 3: Cubic Convolution Resampling

Raster Tab The last tab on the Options dialog is Raster.

Figure 12: Raster Tab


Uses 4 x 4 resampling. In most cases, the mean and standard deviation of the output pixels match the mean and standard deviation of the input pixels more closely than any other resampling method.

Can result in altered data values.

Can both sharpen the image and smooth out noise (Atkinson 1985), due to the effect of the cubic curve weighting. The actual effects depends on the data you use.

Slowest resampling method because it is the most computationally intensive.

Recommended for use when you are dramatically changing the cell size of the data, such as in TM/aerial photo merges (that is, matches the 4 x 4 window more closely than the 2 x 2 window).


There are several raster formats with differences between the support offered by ESRI and by ERDAS. These differences can be attributed to a variant of that format, how data is stored, or the amount of data that can be read to improve the display accuracy of that file. In some cases, ERDAS lets you use ERDAS libraries to support a certain format. This ensures you the same level of format support as in the past. Note: It is recommended that you leave the supported formatting options enabled because disabling a format results in that format’s handling being delivered by ESRI.The following formats are supported:

• SOCET SET

• IMAGINE Image

• TIFF

• NITF

Using the Options Dialog The following steps take you through the settings you can change in the Options dialog. You can display the Options dialog by selecting Options from the Image Analysis dropdown list.

Using the General TabTo specify settings on the General tab, follow these steps:

1. Click the General tab on the Options dialog.

2. Click the browse button for the Working Directory field and navigate to your working directory.

3. Select an analysis mask from the Analysis Mask dropdown list, or navigate to a directory and select one.

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4. Select a setting from the Analysis Coordinate System dropdown list.Note: If you select As Specified below, the button below it becomes active. Click the button to open the Spatial Reference Properties dialog, specify your coordinate system settings, and then click OK.

5. Click the Extent tab to change analysis extents, or click Apply and OK to close the Options dialog.

Using the Extent TabTo specify settings on the Extent tab, follow these steps:

1. Select an extent from the Analysis Extent dropdown list, or navigate to a directory and select a dataset for the extent.

2. Type coordinates in the Top, Left, Right, and Bottom fields if you selected As Specified Below from the Analysis Extent dropdown list.

3. Select an image from the Snap Extent To dropdown list, or navigate to the directory where it is stored if you activated this option by selecting As Specified below or Same as Display from the Analysis Extent dropdown list.

4. Click the Cell Size tab to change cell sizes, or click Apply and OK to close the Options dialog.

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Using the Cell Size TabTo specify settings on the Cell Size tab, follow these steps:

1. Select the output cell size from the Analysis Cell Size dropdown list, or navigate to a directory and select a file on which to base the output cell size.

2. Type a cell size in the Cell Size field if you selected As Specified Below from the Analysis Cell Size dropdown list.

3. Change the number in the Number of Rows field if necessary.

4. Change the number in the Number of Columns field if necessary.

5. Click the Preferences tab to change preferences, or click Apply and OK to close the Options dialog.

Using the Preferences TabThe Preferences tab on the Options dialog has only one option that lets you resample using either Nearest Neighbor, Bilinear Interpolation, or Cubic Convolution using the Resample Using dropdown list. Bilinear Interpolation is the default, and it is recommended that you leave the preference to this default except for thematic data processing, in which case you should change it to Nearest Neighbor.

Using the Raster TabThe Raster tab on the Options dialog has several raster formatting options that are already enabled. It is recommended that you leave the supported formatting options enabled because disabling a format results in that format’s handling being delivered by ESRI.

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Geoprocessing Tools

In Image Analyst for ArcGIS, all functions with the exception of Single Frame GeoCorrection and the Seed tool, are provided as geoprocessing tools. This lets you take full advantage of the ArcGIS geoprocessing environment. Benefits include:

• Ability to do image processing in ArcCatalog or ArcToolbox in a scripting environment.

• Ability to chain Image Analysis processes into a workflow or analysis model.

• Opportunity to integrate with other geoprocessing tools or scripts such as those provided by ESRI and third-party developers.

• Tools that respect the settings made in the ArcGIS geoprocessing environment. Examples of the environment settings are current workspace, cell size, analysis extent, output coordinate system, and resampling method.

If you are new to geoprocessing, we suggest that you read the ArcGIS Desktop online help topics for geoprocessing.

Specifying Geoprocessing Options

In earlier versions of Image Analysis for ArcGIS, the Image Analysis Options dialog saved the analysis settings to the map document (.mxd). Now, the Options dialog provides a more convenient alternative to using the application-wide geoprocessing Environment Settings dialog. It lets you view and alter the environment settings that are most relevant to Image Analysis for ArcGIS functions without having to navigate all of the geoprocessing environment settings presented by the Environment Settings dialog.You can use either Image Analysis for ArcGIS dialog—the Options dialog or the Environment Settings dialog—as a valid method for specifying options for processing.

Updating Existing Geoprocessing Models

The Image Analysis for ArcGIS geoprocessing functions were rebuilt during the time frame of the current version of ArcGIS. It is recommended that you update existing models with the new geoprocessing functions. To do this, open the model containing functions from the earlier version and replace each with the current version.

Using Data Preparation 71

Using Data PreparationIt is sometimes necessary to prepare your data before working with images. However, you must understand how to prepare your data before manipulating and analyzing it. You have several options for preparing data in Image Analysis for ArcGIS.IN THIS CHAPTER

• Create New Image

• Subset Image

• Mosaic Images

• Reproject Image

72 Using Data Preparation

Create New Image The create new image function makes it easy to create a new image file. It lets you define the size and content of the file. It also lets you specify whether the new image type is thematic or continuous:

• Thematic Data – Represented in raster layers that contain qualitative and categorical information about an area. Thematic layers lend themselves to applications in which categories or themes are used. They represent data measured on a nominal or ordinal scale, such as soils, land use, land cover, and roads. Thematic data is also known as discrete data.

• Continuous Data – Represented in raster layers that contain quantitative (measuring a characteristic on an interval or ratio scale) and related, continuous values. Continuous raster layers can be multiband or single band such as Landsat, SPOT, digitized (scanned) aerial photograph, DEM, slope, and temperature.

The table below summarizes the values appropriate for the various data types.

The create new image function also provides these options:

• Number of Rows and Columns – Lets you specify the number of rows and columns (the default is 512), and the data type.

• Data Type – Determines the type of numbers and the range of values that you can store in a raster layer.

• Number of Layers – Lets you select how many layers to create in the new file.

Table 4: Data Type Values

Data Type Minimum Value

Maximum Value

Unsigned 1 bit 0 1

Unsigned 2 bit 0 3

Unsigned 4 bit 0 15

Unsigned 8 bit 0 255

Signed 8 bit -128 127

Unsigned 16 bit 0 65,535

Signed 16 bit -32,768 32,767

Unsigned 32 bit

Signed 32 bit -2 billion 2 billion

Float Single


• Initial Value – Lets you specify the value given to every cell in the new file.

Creating a New Image To create a new image, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Data Preparation, and then select Create New Image to open the Create New Image dialog.

2. Click the browse button for the Output Image field and navigate to the directory where you want the output image stored.

3. Click either the Thematic or Continuous button as the output image type.

4. Type the number of columns and rows, if different from the default number of 512, in the Columns and Rows fields.

5. Select a data type from the Data Type dropdown list.

6. Type the number of layers that you want in the Number of Layers field.

7. Type the initial pixel value in the Initial Value field.

8. Click OK to create the image and close the Create New Image dialog.

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Subset Image The subset image function lets you copy a portion (a subset) of an input data file into an output data file. This may be necessary if you have an image file that is much larger than the area you need to study. Subsetting an image has the advantage of eliminating extraneous data and speeding up processing by reducing the file size. This is important when dealing with multiband data.You can use subset image to subset an image either spatially or spectrally:

• Spatially – Subset an image spatially by setting an analysis mask using the Subset Image dialog or set an extent using the Options dialog or the Environment Settings dialog. Spatial subsets are particularly useful if you have a large image and you only want to subset part of it for analysis.

• Spectrally – Subset an image spectrally by using the subset image function to work on multiband continuous data to remove specific bands. For example, if you are working with a thematic mapper (TM) image that has seven bands of data, you can make a subset of bands 2, 3, and 4, and discard the rest. Use the Subset Image dialog to enter the band numbers to extract from the image.

If you want to specify a particular area to subset, click the Zoom In tool and draw a rectangle over the area. Next, display the Options dialog and select Same As Display from the Analysis Extent dropdown list on the Extent tab.

Figure 13: Extent Tab


The following are illustrations of a TM image of the Amazon as it undergoes a spectral subset.

Figure 14: Amazon TM Image before Spectral Subsetting

Figure 15: Amazon TM Image after Spectral Subsetting


The illustrations that follow reflect images using the Spatial Subsetting option.

Figure 16: Pentagon Image before Spatial Subsetting

The rectangle is defined by Top, Left, Bottom, and Right coordinates. Top and Bottom coordinates are measured as the locations on the Y-axis and the Left and Right coordinates are measured on the X-axis. You can then save the subset image and work from there on your analysis.

Figure 17: Pentagon Subset Image after Analysis Extent


Subsetting an Image Spectrally

To subset an image spectrally, follow these steps:

1. Click the Add Data button and add the image you want to subset to the view.


3. Click the browse button for the Input Image field and navigate to the directory where your image is located.

4. Type the number of bands you want present in your output in the Select Desired Band Numbers field, using a comma for separation.


6. Click OK to close the Subset Image dialog.

Subsetting an Image Spatially

To subset an image spatially, follow these steps:

1. Click the Add Data button to add your image to the view.

2. Click the Zoom In tool, and draw a rectangle over the area you want to subset.

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4. Click the Environments button to open the Environment Settings dialog.

5. Click the General Settings arrow.

6. Select Same as Display from the Extent dropdown list.

7. Click OK to return to the Subset Image dialog.


9. Click OK to close the Subset Image dialog.Note: There are other methods of subsetting. For more information, see the Subset Image topic in the Image Analysis for ArcGIS online help.

Mosaic Images Mosaicking is the process of joining georeferenced images together to form a larger image. The input images must all contain map and projection information, although they need not be in the same projection or have the same cell sizes. Calibrated input images are also supported. You can mosaic single or multiband continuous data, or thematic data.If you have data in the view, it loads into the Mosaic Images dialog in that order, giving you a final image that is identical to the initial view. You can reorder the data in the Mosaic Images dialog.It is important that the images you mosaic contain the same number of bands. You cannot mosaic a seven-band TM image with a six-band TM image. You can, however, use subset image to remove the extra band and then mosaic.

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You can mosaic images with different cell sizes or resolutions. The output cell size defaults to the maximum cell size. For example, if you mosaic two images, one with a 4 m resolution and one with a 5 m resolution, the output mosaicked image has a 5 m resolution. You can set the cell size to whatever cell size you like using the Options dialog or the Environment Settings dialog. However, the data cannot be created to compensate if you specify a higher resolution than your input. You still have the same coarseness of the original file.The Extent tab on the Options dialog defaults to Union of Inputs for mosaicking images. If, for some reason, you want to use a different extent, you can change it in the Options dialog and check the Use Extent from Analysis Options check box in the Mosaic Images dialog. It is recommended that you leave it at the default of Union of Inputs.For mosaicking images, you should resample using the Nearest Neighbor option on the Preferences tab. This ensures that the mosaicked pixels do not differ in their appearance from the original image. Other resampling methods use averages to compute pixel values and can produce an edge effect.With the Mosaic tool you are given a choice of how to handle image overlaps by using the Order Displayed, Maximum Value, Minimum Value, or Average Value settings:

• Order Displayed – Replaces each pixel in the overlap area with the pixel value of the image that is on top in the view.

• Maximum Value – Replaces each pixel in the overlap area with the greater value of corresponding pixels in the overlapping images.

• Minimum Value – Replaces each pixel in the overlap area with the lesser value of the corresponding pixels in the overlapping images.

• Average Value – Replaces each pixel in the overlap area with the average of the values of the corresponding pixels in the overlapping images.

The color balancing settings let you choose between balancing by brightness/contrast, histogram matching, or no color balancing. Select Histogram Matching to adjust the input images to have similar histograms as the top image in the view. Select None if you don’t want the pixel values adjusted.


Mosaicking Images To mosaic an image, follow these steps:

1. Add the images you want to mosaic to your view.

2. Click the Image Analysis dropdown arrow, point to Data Preparation, and then select Mosaic Images to open the Mosaic Images dialog.

3. Arrange the images in the order that you want them in the mosaic using the arrows to the right of the box below the Input Images field.

4. Select the method you want to use from the Handle Image Overlaps By dropdown list.

5. Check the Automatically Crop Images By check box to automatically crop images, and then type the percent by which to crop the images in the Percent field.

6. Click a button for the color balancing in the Color Balance By box.

7. Check the Use Extent from Analysis Options check box if you want to use the extent you set in the Options dialog.


9. Click OK to mosaic the images and close the Mosaic Images dialog.For more information on mosaicking images, see “Quick-Start Tutorial” on page 11.

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Reproject Image Use Reproject Image to reproject raster image data from one map projection to another. The new projection is specified in the Reproject Image dialog.Optionally, you can specify an output coordinate system using the Spatial Reference Properties dialog. You can display this dialog by clicking the button adjacent to the Output Coordinate System field. However, if you do not want to specify an output coordinate system, leave the field blank.

Reprojecting an Image To reproject an image, follow these steps:

1. Click the Add Data button and add the image you want to reproject to the view.

2. Click the Image Analysis dropdown arrow, point to Data Preparation, and then select Reproject Image to open the Reproject Image dialog.

3. Select the file you want to use from the Input Image dropdown list, or click the browse button and navigate to the directory where it is stored.

4. Click the button for the Output Coordinate System field to open the Spatial Reference Properties dialog.

5. Specify your coordinate system settings, and then click OK to return to the Reproject Image dialog.


7. Click OK to close the Reproject Image dialog.

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83Performing Spatial Enhancement

Performing Spatial Enhancement 83

Performing Spatial EnhancementSpatial enhancement is a function that enhances an image using the values of individual and surrounding pixels. Spatial enhancement deals largely with spatial frequency, which is the difference between the highest and lowest values of a contiguous set of pixels. Jensen (1986) defines spatial frequency as “the number of changes in brightness value per unit distance for any part of an image.”There are three types of spatial frequency:

• Zero Spatial Frequency – A flat image in which every pixel has the same value.

• Low Spatial Frequency – An image consisting of a smoothly varying gray scale.

• High Spatial Frequency – An image consisting of drastically changing pixel values such as a checkerboard of black-and-white pixels.

Spatial enhancement has functions for convolution filtering, non-directional edge, focal analysis, and resolution merge to enhance your images. This chapter focuses on the explanation of these features as well as how to apply them to your data. It is organized according to the order in which the spatial enhancement tools appear. You can skip ahead if the information you seek is about one of the tools near the end of the list.IN THIS CHAPTER

• Convolution

• Non-Directional Edge

• Focal Analysis

• Resolution Merge

84 Performing Spatial Enhancement

Convolution Convolution filtering is the process of averaging small sets of pixels across an image. Convolution filtering is used to change the spatial frequency characteristics of an image (Jensen 1996). The word filtering is a broad term that refers to the altering of spatial or spectral features for image enhancement (Jensen 1996). Convolution filtering is one method of spatial filtering. Some texts use the terms synonymously.A convolution kernel is a matrix of numbers used to average the value of each pixel with the values of surrounding pixels. The numbers in the matrix weigh this average toward particular pixels. These numbers are often called coefficients because they are used as such in the mathematical equations.

Applying convolution Filtering

Apply convolution filtering by clicking the Image Analysis dropdown arrow and selecting Convolution from the Spatial Enhancement menu.

Convolution Example You can understand how one pixel is convolved by imagining that the convolution kernel is overlaid on the data file values of the image (in one band) so that the pixel being convolved is in the center of the window.

2 8 6 6 6

2 8 6 6 6

2 2 8 6 6

2 2 2 8 6

2 2 2 2 8

Kernel

-1 -1 -1

-1 16 -1

-1 -1 -1

Data


Compute the output value for this pixel by multiplying each value in the convolution kernel by the image pixel value that corresponds to it. These products are summed, and the total is divided by the sum of the values in the kernel, as shown in this equation:

integer [((-1 × 8) + (-1 × 6) + (-1 × 6) + (-1 × 2) + (16 × 8) + (-1 × 6) + (-1 × 2) + (-1 × 2) + (-1 × 8))/ : (-1 + -1 + -1 + -1 + 16 + -1 + -1 + -1 + -1)]= int [(128-40) / (16-8)] = int (88 / 8) = int (11) = 11

When the 2 x 2 set of pixels near the center of this 5 x 5 image is convolved, the output values are:

The kernel used in this example is a high-frequency kernel. The relatively lower values become lower, and the higher values become higher, thus increasing the spatial frequency of the image.

Convolution Formula The following formula is used to derive an output data file value for the pixel being convolved (in the center):

Where:fij = The coefficient of a convolution kernel at position i,j (in thekernel)dij = The data value of the pixel that corresponds to fij q= The dimension of the kernel, assuming a square kernel (if q =3, the kernel is 3 x 3)F = Either the sum of the coefficients of the kernel, or 1 if the sum of coefficients is 0V = The output pixel value

1 2 3 4 5

1 - - - - -

2 - 11 5 - -

3 - 0 11 - -

4 - - - - -

5 - - - - -

V

fijdijj 1=

q

∑

i 1=

q

∑

F---------------------------------------------=


Source: Modified from Jensen 1996; Schowengerdt 1983The sum of the coefficients (F) is used as the denominator of the equation above so that the output values are in relatively the same range as the input values. Because F cannot equal 0 (division by 0 is not defined), F is set to 1 if the sum is 0.

Zero Sum Kernels Zero sum kernels are kernels in which the sum of all coefficients in the kernel equals 0. When a zero sum kernel is used, the sum of the coefficients is not used in the convolution equation, as shown above. In this case, no division is performed (F = 1), because division by 0 is not defined.This generally causes the output values to be:

• Zero in areas where all input values are equal (no edges)

• Low in areas of low spatial frequency

• Extreme in areas of high spatial frequency (high values become much higher, low values become much lower)

Therefore, a zero sum kernel is an edge detector, which usually smooths out or zeros out areas of low spatial frequency and creates a sharp contrast where spatial frequency is high, which is at the edges between homogeneous (homogeneity is low spatial frequency) groups of pixels. The resulting image often consists of only edges and zeros.Zero sum kernels can be biased to detect edges in a particular direction. For example, this 3 x 3 kernel is biased to the south (Jensen 1996).

High-Frequency Kernels A high-frequency kernel, or high-pass kernel, has the effect of increasing spatial frequency. High-frequency kernels serve as edge enhancers because they bring out the edges between homogeneous groups of pixels. Unlike edge detectors (such as zero sum kernels), they highlight edges and do not necessarily eliminate other features.

-1 -1 -1

1 -2 1

1 1 1

-1 -1 -1

-1 16 -1

-1 -1 -1


When a high-frequency kernel is used on a set of pixels in which a relatively low value is surrounded by higher values, like this....

...the low value gets lower. Inversely, when the high-frequency kernel is used on a set of pixels in which a relatively high value is surrounded by lower values...

...the high value becomes higher. In either case, spatial frequency is increased by this kernel.

Low-Frequency Kernels Below is an example of a low-frequency kernel, or low-pass kernel, which decreases spatial frequency.

This kernel averages the values of the pixels, causing them to be more homogeneous. The resulting image looks either more smooth or more blurred.

Figure 18: Convolution with High-Pass Filtering

BEFORE AFTER

204 200 197 - - -

201 106 209 - 10 -

198 200 210 - - -

BEFORE AFTER

64 60 57 - - -

61 125 69 - 188 -

58 60 70 - - -

1 1 1

1 1 1

1 1 1


Applying Convolution To apply convolution, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Spatial Enhancement, and then select Convolution to open the Convolution dialog.

2. Select a file from the Input Image dropdown list, or navigate to the directory where the file is stored.

3. Select a kernel to use from the Kernel dropdown list.

4. Click either the Reflection or Background Fill button to specify the way to handle edges in the image. See “Using Convolution” for more information.


Convolution with High Pass

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6. Click OK to close the Convolution dialog.

Using ConvolutionConvolution lets you perform image-enhancement operations such as averaging and high-pass or low-pass filtering. Reflection fills in the area beyond the edge of the image with a reflection of the values at the edge. Background Fill uses zeros to fill in the kernel area beyond the edge of the image. Each data file value of the new output file is calculated by centering the kernel over a pixel and multiplying the original values of the center pixel and the appropriate surrounding pixels by the corresponding coefficients from the matrix. These numbers are summed and then divided by the sum of the coefficients to ensure the output values are within the general range of the input values. If the sum is zero, the division is not performed.

Non-Directional Edge

The non-directional edge function averages the results of two orthogonal first derivative edge detectors, using the Sobel and Prewitt filters. The filters are based on a calculation of the 1st derivative, or slope, in both the X and Y directions. Both use orthogonal kernels convolved separately with the original image and then combined.The non-directional edge function is based on the Sobel zero sum convolution kernel. Most of the standard image processing filters are implemented as a single-pass moving window (kernel) convolution. Examples include low-pass, edge-enhance, edge-detection, and summary filters. For this model, a Sobel filter is used. To convert this model to the Prewitt filter calculation, change the kernels according to the example below.

1 0 1–2 0 2–1 0 1–

Vertical

1– 2– 1–0 0 01 2 1

Horizontal

Sobel=

1 0 1–1 0 1–1 0 1–

Vertical

1– 1– 1–0 0 01 1 1

Horizontal

Prewitt=


Figure 19: Image of Seattle before Non-Directional Edge

Figure 20: Image of Seattle after Non-Directional Edge


Applying Non-Directional Edge

To apply non-directional edge, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Spatial Enhancement, and then select Non-Directional Edge to open the Non-Directional Edge dialog.


3. Click either the Sobel or Prewitt button to specify the filter to use.

4. Click either the Reflection or Background Fill button to specify the way to handle edges in the image. For more information, see “Using Non-Directional Edge”.


6. Click OK to close the Non-Directional Edge dialog.

Using Non-Directional EdgeIn step 4 in the previous section, Reflection fills in the area beyond the edge of the image with a reflection of the values at the edge. Background Fill uses zeros to fill in the kernel area beyond the edge of the image.

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Focal Analysis The focal analysis function lets you perform one of several types of analysis on class values in an image file using a process similar to convolution filtering.This model (Median Filter) is useful for reducing noise such as random spikes in data sets, dead sensor striping, and other impulse imperfections in any type of image. It is also useful for enhancing thematic images.Focal analysis evaluates the region surrounding the pixel of interest (center pixel). The operations you can perform on the pixel of interest include:

• Standard Deviation (measure of texture)

• Sum

• Mean (good for despeckling radar data)

• Median (despeckle radar)

• Min

• Max

These functions let you select the size of the surrounding region to evaluate by selecting the window size.

Figure 21: Image before Focal Analysis


Figure 22: Image after Focal Analysis

Applying Focal Analysis To apply focal analysis, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Spatial Enhancement, and then select Focal to open the Focal Analysis dialog.


3. Select the function to use from the Focal Function dropdown list.

4. Select a shape from the Neighborhood Shape dropdown list.

5. Select a matrix size from the Neighborhood Definition - Matrix Size dropdown list.

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7. Click OK to close the Focal Analysis dialog.

Using Focal AnalysisFocal analysis is similar to convolution in the process it uses. With focal analysis, you can perform several different types of analysis on the pixel values in an image file.

Resolution Merge The resolution of a specific sensor can refer to radiometric, spatial, spectral, or temporal resolution. This function merges imagery of differing spatial resolutions.Landsat TM sensors have seven bands with a spatial resolution of 28.5 m. SPOT panchromatic has one broad band with a very good spatial resolution of 10 m. Combining these two images to yield a seven-band data set with 10 m resolution provides the best characteristics of both sensors.A number of models have been suggested to achieve this image merge:

• Welch and Ehlers (1987) use forward-reverse RGB to IHS transforms, replacing I (from transformed TM data) with the SPOT panchromatic image. However, this technique is limited to three bands (RGB).

• Chavez (1991), among others, uses the forward-reverse principal components transforms with the SPOT image, replacing PC-1.

In the above two techniques, it is assumed that the intensity component (PC-1 or I) is spectrally equivalent to the SPOT panchromatic image, and that all the spectral information is contained in the other PCs or in H and S. Because SPOT data does not cover the full spectral range that TM data does, this assumption does not strictly hold. It is unacceptable to resample the thermal band (TM6) based on the visible (SPOT panchromatic) image.Another technique (Schowengerdt 1980) additively combines a high-frequency image derived from the high-spatial resolution data (that is, SPOT panchromatic) with the high-spectral resolution Landsat TM image.

Brovey Transform The resolution merge function uses the Brovey Transform method of resampling low spatial resolution data to a higher spatial resolution while retaining spectral information.In the Brovey Transform, three bands are used according to the following formula:


DNB1_new = [DNB1 / DNB1 + DNB2 + DNB3] × [DNhigh res. image]DNB2_new = [DNB2 / DNB1 + DNB2 + DNB3] × [DNhigh res. image] DNB3_new = [DNB3 / DNB1 + DNB2 + DNB3] × [DNhigh res. image]

Where:B = band

The Brovey Transform was developed to visually increase contrast in the low and high ends of an image’s histogram (that is, to provide contrast in shadows, water, and high-reflectance areas such as urban features). Brovey Transform is good for producing RGB images with a higher degree of contrast in the low and high ends of the image histogram and for producing visually appealing images.Because the Brovey Transform is intended to produce RGB images, you should merge only three bands at a time from the input multispectral scene, such as bands 3, 2, 1 from a SPOT or Landsat TM image or 4, 3, 2 from a Landsat TM image. You should display the resulting merged image with bands 1, 2, 3 to RGB.

Applying Resolution Merge

To apply resolution merge, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Spatial Enhancement, and then select Resolution Merge to open the Resolution Merge dialog.

2. Select a file from the High Resolution Image dropdown list, or navigate to the directory where the file is stored.

3. Select a file from the Multi-Spectral Image dropdown list, or navigate to the directory where the file is stored.


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5. Click OK to close the Resolution Merge dialog.

Using Resolution MergeUse resolution merge to integrate imagery of different spatial resolutions (pixel size). The following images display the inputs and result of the resolution merge function. The first image is a high-resolution image, the second image is a multi-spectral image, and the bottom image in a resolution merge.

Figure 23: High-Resolution, Multi-spectral, and Resolution Merge Images

97Using Radiometric Enhancement

Using Radiometric Enhancement 97

Using Radiometric EnhancementRadiometric enhancement deals with the individual values of pixels in an image. It differs from spatial enhancement, which takes into account the values of neighboring pixels.Radiometric enhancement contains functions to enhance your image by using the values of individual pixels in each band. Depending on the points and the bands in which they appear, radiometric enhancements that are applied to one band might not be appropriate for other bands. Therefore, the radiometric enhancement of a multiband image can usually be considered as a series of independent, single-band enhancements (Faust 1989).IN THIS CHAPTER

• LUT Stretch

• Histogram Equalization

• Histogram Matching

• Brightness Inversion

98 Using Radiometric Enhancement

LUT Stretch LUT stretch creates an output image that contains the data values as modified by a lookup table (LUT). The output is three bands.

Contrast Stretch When radiometric enhancements are performed on the display device, the transformation of data file values into brightness values is illustrated by the graph of a lookup table. Contrast stretching involves taking a narrow input range and stretching the output brightness values for those same pixels over a wider range. This process is done using the Layer Properties dialog in ArcGIS.

Linear and Nonlinear The terms linear and nonlinear, when describing types of spectral enhancement, refer to the function applied to data to perform the enhancement. A piecewise linear stretch uses a polyline function to increase contrast to varying degrees over different ranges of the data.

Linear Contrast Stretch A linear contrast stretch is a simple way to improve the visible contrast of an image. It is often necessary to contrast stretch raw image data so you can see it on the display device.In most raw data, the data file values fall within a narrow range—usually a range much narrower than the display device is capable of displaying. That range can be expanded to utilize the total range of the display device (usually 0 to 255).

Nonlinear Contrast Stretch

A nonlinear spectral enhancement gradually increases or decreases contrast over a range, instead of applying the same amount of contrast (slope) across the entire image. Usually, nonlinear enhancements bring out the contrast in one range while decreasing the contrast in other ranges.

Piecewise Linear Contrast Stretch

A piecewise linear contrast stretch allows for the enhancement of a specific portion of data by dividing the lookup table into three sections: low, middle, and high. It lets you create a number of straight-line segments that can simulate a curve. You can enhance the contrast or brightness of any section in a single color gun at a time. This technique is very useful for enhancing image areas in shadow or other areas of low contrast. A piecewise linear contrast stretch normally follows two rules:

1. The data values are continuous; there can be no break in the values between high, middle, and low. Range specifications adjust in relation to any changes to maintain the data value range.

2. The data values specified can go only in an upward, increasing direction.


The contrast value for each range represents a percentage of the available output range that particular range occupies. Because rules 1 and 2 above are enforced, the contrast and brightness values may affect the contrast and brightness of other ranges as they are changed. For example, if the contrast of the low range increases, it forces the contrast of the middle range to decrease.

Contrast Stretch on the Display

Usually, a contrast stretch is performed on the display device only, so that the data file values are not changed. Lookup tables are created that convert the range of data file values to the maximum range of the display device. You can then edit and save the contrast stretch values and lookup tables as part of the raster data image file. These values are loaded into view as the default display values the next time the image is displayed.The statistics in the image file contain the mean, standard deviation, and other statistics on each band of data. The mean and standard deviation are used to determine the range of data file values to translate into brightness values or new data file values. You can specify the number of standard deviations from the mean to use in the contrast stretch. Usually the data file values that are two standard deviations above and below the mean are used. If the data has a normal distribution, this range represents approximately 95 percent of the data.The mean and standard deviation are used instead of the minimum and maximum data file values because the minimum and maximum data file values do not usually represent most of the data. A notable exception occurs when the feature being sought is in shadow. The shadow pixels are usually at the low extreme of the data file values, outside the range of two standard deviations from the mean.

Varying the Contrast Stretch

There are variations of the contrast stretch you can use to change the contrast of values over a specific range, or by a specific amount. By manipulating the lookup tables as in the following illustration, you can bring out the maximum contrast in the features of an image.


This figure shows how the contrast stretch manipulates the histogram of the data, increasing contrast in some areas and decreasing it in others. This is also a good example of a piecewise linear contrast stretch, which is created by adding breakpoints to the histogram.

Figure 24: Contrast Stretch

Applying a LUT Stretch To apply a LUT stretch, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Radiometric Enhancement, and then select LUT Stretch to open the LUT Stretch dialog.

2. Select the file you want to use from the Input Image dropdown list, or navigate to the directory where it is stored.

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4. Set the output type to TIFF.

5. Click OK to close the LUT Stretch dialog.

Using LUT Stretch LUT stretch provides a means of producing an output image that has the stretch built into the pixel values to use with packages that have no stretching capabilities.

Histogram Equalization

Histogram equalization is a nonlinear stretch that redistributes pixel values so that there is approximately the same number of pixels with each value in a range. The result approximates a flat histogram. Therefore, contrast is increased at the peaks of the histogram and lessened at the tails.Histogram equalization can also separate pixels into distinct groups if there are few output values over a wide range. This can have the visual effect of a crude classification.

Original Histogram

After Equalization

Peak

Tail

Pixels at peak are spreadapart—contrast is gained

Pixels attail aregrouped—contrastis lost


When performing a histogram equalization, the pixel values of an image (either data file values or brightness values) are reassigned to a certain number of bins, which are numbered sets of pixels. The pixels are then given new values based on the bins to which they are assigned.The total number of pixels is divided by the number of bins, equaling the number of pixels per bin, as shown in the following equation:

Where:N = Number of binsT = The total number of pixels in the imageA = The equalized number of pixels per bin

The pixels of each input value are assigned to bins, so that the number of pixels in each bin is as close to A as possible. Consider the following:

There are 240 pixels represented by this histogram. To equalize it to 10 bins, there would be:

240 pixels / 10 bins = 24 pixels per bin = A

A TN----=

0 1 2 3 4 5 6 7 8 9

5 510

15

60 60

40

30

105

Num

ber o

f Pix

els

Data File Values

A = 24


The following equation is used to assign pixels to bins:

Where: A =Equalized number of pixels per bin (see above) Hi =The number of values with the value i (histogram) int=Integer function (truncating real numbers to integer) Bi=Bin number for pixels with value i

Source: Modified from Gonzalez and Wintz 1977The 10 bins are rescaled to the range 0 to M. In this example, M = 9 because the input values ranged from 0 to 9 so that the equalized histogram can be compared to the original. The output histogram of this equalized image looks like the following illustration:

Effect on Contrast By comparing the original histogram of the example data with the last one, you can see that the enhanced image gains contrast in the peaks of the original histogram. For example, the input range of 3 to 7 is stretched to the range 1 to 8. However, data values at the tails of the original histogram are grouped together. Input values 0 through 2 all have the output value of 0. So, contrast among the tail pixels, which usually make up the darkest and brightest regions of the input image, is lost.

Bi int

Hkk 1=

i 1–

∑ Hi

2------+

A----------------------------------------=

0 1 2 3 4 5 6 7 8 9

15

60 60

40

30

Num

ber o

f Pix

els

Output Data File Values

A = 242015

01

2

3

4 5

78

9

6

Numbers inside bars are input data file values

0 0 0


The resulting histogram is not exactly flat because pixels rarely group together into bins with an equal number of pixels. Sets of pixels with the same value are never split up to form equal bins.

Performing Histogram Equalization

To perform histogram equalization, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Radiometric Enhancement, and then select Histogram Equalization to open the Histogram Equalization dialog.


3. Type the number of bins in the Number of Bins field.


5. Click OK to close the Histogram Equalization dialog.

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Using Histogram EqualizationThe histogram equalization process works by redistributing pixel values so that there are approximately the same number of pixels with each value within a range.Histogram equalization can also separate pixels into distinct groups if there are few output values over a wide range. This process can have the effect of a crude classification.

Histogram Matching

Histogram matching is the process of determining a lookup table that converts the histogram of one image so that it resembles the histogram of another. Histogram matching is useful for matching data of the same or adjacent scenes that were collected on separate days, or are slightly different because of sun angle or atmospheric effects. This is especially useful for mosaicking or change detection.The two input images should have similar characteristics to achieve good results with histogram matching:

• The general shape of the histogram curves should be similar.

• Relative dark and light features in the image should be the same.

• For some applications, the spatial resolution of the data should be the same.

• The relative distributions of land covers should be about the same, even when matching scenes that are not of the same area.


When matching the histograms, a lookup table is mathematically derived, which serves as a function for converting one histogram to the other as illustrated here.

Figure 25: Histogram Matching

Performing Histogram Matching

To perform histogram matching, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Radiometric Enhancement, and then select Histogram Match to open the Histogram Match dialog.

Source histogram (A), mapped through the lookup table (B), approximates model histogram (C).

Freq

uenc

y

Input0 255

Freq

uenc

y

Input0 255

Freq

uenc

yInput

0 255

+

=

(A) (B)

(C)

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3. select the file you want to use from the Match Image dropdown list, or navigate to the directory where it is stored.


5. Click OK to close the Histogram Match dialog.

Using Histogram MatchingHistogram matching mathematically determines a lookup table that converts the histogram of one image to resemble the histogram of another, and is particularly useful for mosaicking images or change detection.Perform histogram matching when using matching data of the same or adjacent scenes that were gathered on different days and have differences due to the angle of the sun or atmospheric effects.

Brightness Inversion

The brightness inversion function produces images that have the opposite contrast of the original image. Dark detail becomes light, and light detail becomes dark. Inverse emphasizes detail that might be lost in the darkness of low DN pixels. This function applies the following algorithm:

DNout = 1.0 if 0.0 < DNin < 0.1

DNout =

Figure 26: Image before Brightness Inversion

0.1DNin

if 0.1 < DN < 1


Figure 27: Image after Brightness Inversion


Applying Brightness Inversion

To apply brightness inversion, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Radiometric Enhancement, and then select Brightness Inversion to open the Brightness Inversion dialog.



4. Click OK to close the Brightness Inversion dialog.

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111Applying Spectral Enhancement

Applying Spectral Enhancement 111

Applying Spectral EnhancementSpectral enhancement enhances images by transforming the values of each pixel on a multiband basis. All of the techniques in this chapter require more than one band of data. You can use them to:

• Extract new bands of data that are more interpretable to the eye

• Apply mathematical transforms and algorithms

• Display a wider variety of information in the three available color guns

You can use the features of spectral enhancement to study patterns that can occur with deforestation or crop rotation and to see images in a more natural state. You can also use spectral enhancement to view images in different ways, such as changing the bands in an image from red, green, and blue (RGB) to intensity, hue, and saturation (IHS).IN THIS CHAPTER

• RGB to IHS

• IHS to RGB

• Vegetative Indices

• Color IR to Natural Color

112 Applying Spectral Enhancement

RGB to IHS The color monitors used for image display on image processing systems have three color guns that correspond to RGB, the additive primary colors. When displaying three bands of a multiband data set, the viewed image is considered to be in RGB space.However, it is possible to define an alternate color space that uses IHS as the three positioned parameters (in lieu of RGB). This system is advantageous in that it presents colors more closely as perceived by the human eye:

• Intensity – Refers to the overall brightness of the scene (like PC-1) and varies from 0 (black) to 1 (white).

• Saturation – Represents the purity of color and also varies linearly from 0 to 1.

• Hue – Represents the color or dominant wavelength of the pixel. It varies from 0 at the red midpoint through green and blue back to the red midpoint at 360. It is a circular dimension. In the following image, 0 to 255 is the selected range; it can be defined as any data range. However, hue must vary from 0 to 360 to define the entire sphere (Buchanan 1979).

Figure 28: Variance of Intensity and Hue in RGB to IHS

Saturation

Hue

Intensity


The following algorithm was used in the Image Analysis for ArcGIS RGB to IHS transform (Conrac 1980):

Where:R, G, B= Are each in the range of 0 to 1.0r, g, b=Are each in the range of 0 to 1.0M= Largest value, r, g, or bm= Least value, r, g, or b

At least one of the R, G, or B values is 0, corresponding to the color with the largest value, and at least one of the R, G, or B values is 1, corresponding to the color with the least value.The equation for calculating intensity in the range of 0 to 1.0 is:

The equations for calculating saturation in the range of 0 to 1.0 are:If M = m, S = 0

If I £ 0.5,

If I > 0.5,

R M r–M m–---------------=

G M g–M m–---------------=

B M b–M m–---------------=

I M m+2

----------------=

S M m–M m+----------------=

S M m–2 M– m–------------------------=


The equations for calculating hue in the range of 0 to 360 are:If M = m, H = 0If R = M, H = 60 (2 + b - g)If G = M, H = 60 (4 + r - b)If B = M, H = 60 (6 + g - r)

Where:R, G, B =Are each in the range of 0 to 1.0M = Largest value, R, G, or Bm = Least value, R, G, or B

Converting RGB to IHS To convert RGB to IHS, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Spectral Enhancement, and then select RGB to IHS to open the RGB to IHS dialog.

2. Type the name of the input image in the Input Image field, or navigate to the directory where it is stored.


4. Click OK to close the RGB to IHS dialog.

Using RGB to IHSUsing RGB to IHS applies an algorithm that transforms RGB values to IHS values.

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IHS to RGB IHS to RGB is intended as a complement to the standard RGB to IHS transform. In the IHS to RGB algorithm, a min-max stretch is applied to either intensity (I), saturation (S), or both, so that they more fully utilize the 0 to 1 value range. The values for hue (H), a circular dimension, are 0 to 360. However, depending on the dynamic range of the DN values of the input image, it is possible that I or S or both occupy only a part of the 0 to 1 range. In this model, a min-max stretch is applied to either I, S, or both, so that they more fully utilize the 0 to 1 value range. After stretching, the full IHS image is retransformed back to the original RGB space. As the parameter hue is not modified, it largely defines what we perceive as color, and the resulting image looks very much like the input image.It is not essential that the input parameters (IHS) to this transform are derived from an RGB to IHS transform. You can define I or S as other parameters, set hue at 0 to 360, and then transform to RGB space. This is a method of color coding other data sets.In another approach (Daily 1983), H and I are replaced by low- and high-frequency radar imagery. You can also replace I with radar intensity before the IHS to RGB transform (Holcomb 1993). Chavez evaluates the use of the IHS to RGB transform to resolution merge Landsat TM with SPOT panchromatic imagery (Chavez 1991).The algorithm used by Image Analysis for ArcGIS for the IHS to RGB function is (Conrac 1980):Given: H in the range of 0 to 360; I and S in the range of 0 to 1.0

If I 0.5, If I > 0.5,

The equations for calculating R in the range of 0 to 1.0 are:

If H < 60,

If 60 H < 180,

If 180 H < 240,

If 240 H 360,

M I 1 S+( )=M I S I S( )–+=

m 2 1 M–⋅=

R m M m–( ) H60------

+=

R M=

R m M m–( ) 240 H–60

------------------- +=

R m=


The equations for calculating G in the range of 0 to 1.0 are:

If H < 120,

If 120 H < 180,

If 180 H < 300,

If 300 H 360,

The equations for calculating B in the range of 0 to 1.0 are:

If H < 60,

If 60 H < 120,

If 120 H < 240,

If 240 H < 300,

If 300 H 360,

Converting IHS to RGB To convert IHS to RGB, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Spectral Enhancement, and then select IHS to RGB to open the IHS to RGB dialog.

2. Click the browse button for the Input Image field and navigate to the directory where the input image is stored.


4. Click OK to close the IHS to RGB dialog.

Using IHS to RGBUsing IHS to RGB applies an algorithm that transforms IHS values to RGB values.

G m=

G m M m–( ) H 120–60

------------------- +=

G M=

G m M m–( ) 360 H–60

------------------- +=

B M=

B m M m–( ) 120 H–60

------------------- +=

B M=

B m M m–( ) H 240–60

------------------- +=

B M=

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Vegetative Indices Mapping vegetation is a common application of remotely sensed imagery. To help you find vegetation quickly and easily, Image Analysis for ArcGIS includes a vegetative indices feature.Indices are used to create output images by mathematically combining the DN values of different bands. These may be simplistic:(Band X - Band Y)Or more complex:

In many instances, the indices are ratios of band DN values:

These ratio images are derived from the absorption/reflection spectra of the material of interest. The absorption is based on the molecular bonds in the (surface) material. Thus, the ratio often gives information on the chemical composition of the target.

Applications The main applications of vegetative analysis are as follows:

• Indices are used extensively in vegetation analysis to bring out small differences between various vegetation classes. Often, judiciously chosen indices can highlight and enhance differences that you cannot observe in the display of the original color bands.

• Indices are also used to minimize shadow effects in satellite and aircraft multispectral images. You can generate black-and-white images of individual indices, or a color combination of three ratios.

Examples The following are examples of indices that are preprogrammed in Image Analysis for ArcGIS:

• IR/R (infrared/red)

• SQRT (IR/R)

• Vegetation Index = IR-R

• Normalized Difference Vegetation Index (NDVI) =

• Transformed NDVI (TNDVI) =

Source: Modified from Sabins 1987; Jensen 1996; Tucker 1979The following table shows the infrared (IR) and red (R) band for some common sensors (Tucker 1979, Jensen 1996):

BandX BandY–BandX BandY+------------------------------------------

BandXBandY-----------------

IR R–IR R+----------------

IR R–IR R+---------------- 0.5+


Image Algebra Image algebra is a general term used to describe operations that combine the pixels of two or more raster layers in mathematical combinations. For example, the calculation:

(infrared band) - (red band)

DNir - DNred

yields a simple, yet very useful, measure of the presence of vegetation.Band ratios are also commonly used. These are derived from the absorption spectra of the material of interest. The numerator is a baseline of background absorption, and the denominator is an absorption peak. NDVI is a combination of addition, subtraction, and division.

NDVI =

Table 5: IR and R Bands of Common Sensors

Sensor IR Band R Band

Landsat MSS 4 2

SPOT XS 3 2

Landsat TM 4 3

NOAA AVHRR 2 1

IR R–IR R+----------------


Applying Vegetative Indices

To apply vegetative indices, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Spectral Enhancement, and then select Vegetative Indices to open the Vegetative Indices dialog.


3. Select an appropriate layer from the Near Infrared Band dropdown list.

4. Select an appropriate layer from the Visible Red Band dropdown list.

5. Select an appropriate index from the Desired Index dropdown list.


7. Click OK to close the Vegetative Indices dialog.

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Color IR to Natural Color

This function lets you simulate natural colors from the bands of data from an infrared image so that the output is a fair approximation of a natural color image. You cannot apply this feature to images having only one band of data (for example, grayscale images). It’s for use when you have data only from the near infrared, visible red, and visible green segments of the spectrum.When an image is displayed in natural color, the bands are arranged to approximate the most natural representation of the image in the real world. Vegetation becomes green in color, and water becomes dark in color. Certain bands of data are assigned to the red, green, and blue color guns of your computer monitor to create natural color.

Figure 29: Infrared Image of a Golf Course

Figure 30: Natural Colors after Color IR to Natural Color


Changing Color IR to Natural Color

To change Color IR to Natural Color, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Spectral Enhancement, and then select Color IR to Natural Color to open the Color IR to Natural Color dialog.


3. Select the appropriate band from the Near Infrared Band dropdown list.

4. Select the appropriate band from the Visible Red Band dropdown list.

5. Select the appropriate band from the Visible Green Band dropdown list.


7. Click OK to close the Color IR to Natural Color dialog.

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123Performing GIS Analysis

Performing GIS Analysis 123

Performing GIS AnalysisA geographic information system (GIS) is a unique system designed to enter, store, retrieve, manipulate, and analyze layers of geographic data to produce interpretable information. A GIS should also create reports and maps (Marble 1990). The GIS database might include images, maps, statistical data, or any other data needed in a study. This chapter is about using the different analysis functions in Image Analysis for ArcGIS to better use the images, data, maps, and so on located in a GIS.The central purpose of a GIS is to turn geographic data into useful information—the answers to real-life questions—such as:

• How to redraw political districts in a growing metropolitan area?

• How to monitor the influence of global climatic changes on the Earth’s resources?

• What areas to protect to ensure the survival of endangered species?

Although the term GIS is commonly used to describe software packages, a true GIS includes knowledgeable staff, a training program, budgets, marketing, hardware, data, and software (Walker and Miller 1990). You can use GIS technology in almost any geography-related discipline, from landscape architecture to natural resource management to transportation routing.IN THIS CHAPTER

• Information Versus Data

• Neighborhood Analysis

• Thematic Change

• Summarize Areas

• Recode

124 Performing GIS Analysis

Information Versus Data

Information, as opposed to data, is independently meaningful. It is relevant to a particular problem or question:

• Data – The land cover at coordinate N875250, E757261 has a data file value of 8.

• Information – Land cover with a value of 8 are on slopes too steep for development.

You can enter data into a GIS and produce information. The information you want to derive determines the type of data you must enter. For example, if you are looking for a suitable refuge for bald eagles, zip code data is probably not needed, while land cover data might be useful.For this reason, the first step in any GIS project is usually an assessment of the scope and goals of the study. Once the project is defined, you can begin the process of building the database. Although software and data are commercially available, you must create a custom database for the particular project and study area. Design the database to meet the needs and objectives of the organization.A major step in successful GIS implementation is analysis. In the analysis phase, data layers are combined and manipulated to create new layers and to extract meaningful information from them.Once the database (layers and attribute data) is assembled, the layers are analyzed and new information is extracted. Some information is extracted by looking at the layers and visually comparing them to other layers. However, you can retrieve new information by combining and comparing layers.


Neighborhood Analysis

Neighborhood analysis applies to any image processing technique that takes surrounding pixels into consideration, such as convolution filtering and scanning. This is similar to the convolution filtering performed on continuous data. You can perform several types of analyses, such as boundary, density, mean, and sum.With a process similar to the convolution filtering of continuous raster layers, you can also filter thematic raster layers. The GIS filtering process is sometimes referred to as scanning, but is not to be confused with data capture via a digital camera. Neighborhood analysis is based on local or neighborhood characteristics of the data (Star and Estes 1990).Every pixel is analyzed spatially, according to the pixels that surround it. The number and the location of the surrounding pixels are determined by a scanning window, which is defined by you. These operations are known as focal operations. Neighborhood analysis creates a new thematic layer. There are several types of analyses that you can perform on each window of pixels, as described below:

• Density – Produces the number of pixels that have the same class value as the center (analyzed) pixel. It also measures homogeneity (sameness) based upon the analyzed pixel. This is often useful in assessing vegetation crown closure.

• Diversity – Produces the number of class values that are in the window. Diversity is also a measure of heterogeneity (difference).

• Majority – Produces the class value that represents the majority of the class values in the window. This option operates like a low-frequency filter to clean up a salt-and-pepper layer.

• Maximum – Produces the greatest class value in the window. You can use this to emphasize classes with the higher class values or to eliminate linear features or boundaries.

• Minimum – Produces the least or smallest class value in the window. You can use this option to emphasize classes with low-class values.

• Minority – Produces the least common of the class values in the window. You can use this option to identify the least common classes or to highlight disconnected linear features.

• Rank – Produces the number of pixels in the scan window whose value is less than the center pixel.


• Sum – Totals the class values. In a file where class values are ranked, totaling lets you further rank pixels based on their proximity to high-ranking pixels.

Performing Neighborhood Analysis

To perform neighborhood analysis, follow these steps:

1. Click the Image Analysis dropdown arrow, point to GIS Analysis, and then select Neighborhood to open the Neighborhood Analysis dialog.


3. Select the function you want to use from the Neighborhood Function dropdown list.

4. Select Rectangle from the Neighborhood Shape dropdown list.

5. Select the size you want to use from the Neighborhood Definition - Matrix Size dropdown list.


7. Click OK to close the Neighborhood Analysis dialog.

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Thematic Change Thematic change identifies areas that have undergone change over time. Typically, you use thematic change after performing a classification of your data. By using the categorizations of Before Theme and After Theme in the Thematic Change dialog, you can quantify both the amount and the type of changes that take place over time. Image Analysis for ArcGIS produces a thematic image that has all the possible combinations of change.Thematic change creates an output image from two input classified raster files. The class values of the two input files are organized into a matrix. The first input file specifies the columns of the matrix, and the second one specifies the rows. Zero or background values are not treated special in any way. The number of classes in the output file is the product of the number of classes from the two input files. The classes should be the same in each image and in the same order.

Figure 31: Input Image from 1973

Figure 32: Input Image from 1994

Figure 33: Thematic Change Result


Performing Thematic Change

To perform thematic change, follow these steps:

1. Click the Image Analysis dropdown arrow, point to GIS Analysis, and then select Thematic Change to open the Thematic Change dialog.

2. Click the browse button for the Before Theme field and navigate to the directory where the before theme image is stored.

3. Click the browse button for the After Theme field and navigate to the directory where the after theme image is stored.


5. Click OK to close the Thematic Change dialog.Note: You must use the output from this exercise in the next exercise, Summarize Areas on page 130. Please keep the image displayed in your view if you are going on to that exercise.

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The following illustration is an example of the previous image after undergoing thematic change. In the table of contents, you see the combination of classes from the before and after images.

Figure 34: Image Showing Changes between 1973 and 1994

Summarize Areas Image Analysis for ArcGIS also provides summarize areas as a method of assessing change in thematic data. Once you complete the thematic change analysis, you can use summarize areas to limit the analysis to only a portion of the image and derive quantitative information about that area.Summarize areas works by using a feature theme to compile information about that area in tabular format. Summarize areas produces cross-tabulation statistics that compare class value areas between two thematic files using a single thematic change image, and includes a number of points in common, number of acres (or hectares or square miles) in common, and percentages.An example of using summarize areas is to assist a regional planning office in preparing a study of urban change for certain counties within the jurisdiction or even within one county or city. A file containing a polygonal boundary of the area to inventory is analyzed against a file for the same geographical area containing the land cover categories. The summary report indicates the amount of urban change in that particular area of a larger thematic change.


Applying Summarize Areas

This exercise uses the output of the thematic change exercise in Thematic Change on page 128 as the input. Display the thematic change image in your view before starting this exercise.To apply summarize areas, follow these steps:

1. Click the Image Analysis dropdown arrow, point to GIS Analysis, and then select Summarize Areas to open the Summarize Areas dialog.

2. Select the vector theme you want to use from the Zone Theme dropdown list, or navigate to the directory where it is stored.

3. Select the attribute you want to summarize from the Zone Attribute dropdown list.

4. Select the class theme from the Class Theme dropdown list, or navigate to the directory where it is stored. This is the thematic theme you generated in Thematic Change on page 128.

5. Click the browse button for the Summarize Results Table field to specify a name for the new summarize areas table that is created.

6. Click OK to close the Summarize Areas dialog.When the process completes, the resulting table is added to ArcMap.

7. Click the Source tab in the ArcMap table of contents to see the new table.

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Recode Recoding involves assigning new values to one or more classes of an existing file. Recoding is used to:

• Reduce the number of classes

• Combine classes

• Assign different class values to existing classes

• Write class name and color changes to the Attribute table

When an ordinal, ratio, or interval class numbering system is used, you can use recoding to assign classes to appropriate values. Recoding is often performed to make later steps easier. For example, in creating a model that produces good, better, and best areas, it might be beneficial to recode the input layers so all of the best classes have the highest class values.You can also use recode to save any changes made to the color scheme or class names of a classified image to the Attribute table for later use. Just saving an image does not record these changes.Recoding an image involves two major steps:

1. First, you arrange the discrete classes into common groups.

2. Secondly, you perform the actual recoding process, which rewrites the Attribute table using the information from your grouping process.The three recoding methods described in the exercises that follow this section are more accurately described as three methods of grouping the classified image to get it ready for the recode process. These methods are:

• Recoding by class name

• Recoding by symbology

• Recoding a previously grouped image


The following is a thematic image of South Carolina soil types before recode by class name.

Figure 35: Thematic Image before Recode by Class Name

The following is a thematic image of South Carolina soil types after the recode. The changed and grouped class names are listed in the table of contents.

Figure 36: Thematic Image after Recode by Class Name


Recoding by Class Name You must first group the classified image in the ArcMap table of contents and then perform the recode.To recode by class name, follow these steps:

1. Click the Add Data button and add a classified image to your view.

2. Identify the classes you want to group in the table of contents.

3. Right-click the image name and select Properties.

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The Layer Properties dialog opens.


5. Click the Unique Values category.

6. Rename the classes in the Label column in the table that appears in the middle of the tab so that all of the classes you group together have the same class name.

7. Double-click the color bar in the Symbol column for each class and change the colors to reflect the color scheme you want.The image in your view should now look the way you want. Note: The Attribute table is not updated with the new class names.

8. Click Apply, and then click OK to apply your changes and close the Layer Properties dialog.

9. Click the Image Analysis dropdown arrow, point to GIS Analysis, and then select Recode.

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The Recode dialog opens.

10. Type the name of the output image in the Output Image field, or click the browse button and navigate to the directory where you want the output image stored and type a name.

11. Click OK to close the Recode dialog.

Recoding by Symbology This process shows you how to recode by symbology. You see similarities with recoding by class name, but be aware of some different procedures.To record by symbology, follow these steps:

1. Click the Add Data button and display a classified image in your view.

2. Identify the classes you want to group together.

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3. Double-click the image name in the table of contents.

The Layer Properties dialog opens.


5. Click Unique Values in the Show list on the left side of the tab to expand the category.

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6. Press the Ctrl key while clicking the first set of classes you want to group together.

7. Right-click the selected classes and select Group Values.

8. Click in the Label column and type a new name for the class.

9. Follow steps steps 5 - 8 to group the rest of your classes.




13. Click OK to close the Recode dialog.

Recoding with Previously Grouped Image

You may need to open an image that has been classified and grouped in another program such as ERDAS IMAGINE®. These images might have more than one valid attribute column that you can use to perform the recode.To recode using a previously grouped image, follow these steps:

1. Click the Add Data button and add the grouped image to your view.


3. Click the browse button for the Input Image field to navigate to the directory where your image is located and select the image.

4. Select an attribute category, which must be an integer field, that you want to use to recode the image from the Map Pixel Value through Field dropdown list.

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5. Type the name of the output file in the Output Image field, or click the browse button and navigate to the directory where you want the output image stored and type a name.

6. Click OK to close the Recode dialog.The following images depict soil data that was previously grouped in ERDAS IMAGINE.

Figure 37: Soil Data Image before Recode

Figure 38: Soil Data Image after Recode

139Using Utilities

Using Utilities 139

Using UtilitiesAt the core of Image Analysis for ArcGIS is its ability to interpret and manipulate your data. The Utilities section of Image Analysis for ArcGIS provides a number of features for you to use in this capacity. These features let you alter your images to see differences, set new parameters, create images, or change the data type of your image.IN THIS CHAPTER

• Image Difference

• Layer Stack

• Rescale Image

140 Using Utilities

Image Difference Image difference gives you the ability to conveniently perform change detection on aspects of an area by comparing two images of the same place from different times.The Image Difference tool is particularly useful in plotting environmental changes such as urban sprawl and deforestation or the destruction caused by a wildfire or tree disease. It is also a handy tool to use in determining crop rotation or to identify a new neighborhood that needs to be added to an existing GIS.With image difference, you can highlight specific areas of change. The following describes two images generated from image-to-image comparison. One is a grayscale continuous image, and the other is a five-class thematic image.

• Image Difference Image – This is the first image. It is a grayscale image composed of single-band continuous data. This image is created by subtracting the before image from the after image. Because image difference calculates change in brightness values over time, the difference image reflects that change using a grayscale image. Brighter areas have increased in reflectance. This might mean an area cleared of forest. Dark areas have decreased in reflectance. This might mean an area with more vegetation, or an area that was dry and is now wet.

• Highlight Change Image – This is the second image. It is a thematic image that divides the changes into five categories: Decreased, Some Decrease, Unchanged, Some Increase, and Increased. Decreased represents areas of negative (darker) change greater than the threshold for change and is red in color. Increased shows areas of positive (brighter) change greater than the threshold and is green in color. Other areas of positive and negative change less than the thresholds and areas of no change are transparent. You can change the colors for your application.

Using Utilities 141

Figure 39: Image Difference File

Figure 40: Highlight Change File

142 Using Utilities

Using Image Difference To use image difference, follow these steps:

1. Add the two files you want to compare to your view.

2. Click the Image Analysis dropdown arrow, point to Utilities, and then select Image Difference to open the Image Difference dialog.

3. Click the browse button for the Before Theme field and navigate to the directory where the file is stored.

4. Select a layer from the Before Layer dropdown list.

5. Select the file you want to use from the After Theme dropdown list, or navigate to the directory where it is stored.

6. Click the browse button for the After Layer field and navigate to the directory where the layer you want to use is stored.

7. Click either the As Percent or As Value button in the Highlight Changes box.

8. Type values in the Increases More Than and Decreases More Than fields.

9. Click a color bar and select the color you want to represent the increases and decreases.

10. Click the browse button for the Image Difference File field and navigate to the directory where you want the output image stored.

11. Click the browse button for the Highlight Change File field and navigate to the directory where you want it stored.

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Using Utilities 143

12. Click OK to close the Image Difference dialog.

Layer Stack Layer stack lets you stack layers from different images in any order to form a single theme. It is useful for combining different types of imagery for analysis such as multispectral and radar data. For example, if you stack three single-band grayscale images, you finish with one three-band image. In general, stacking images is most useful for combining grayscale single-band images into multiband images.There are several applications of layer stack, such as change visualization, combining and viewing multiple resolution data, and viewing disparate data types. It is particularly useful if you receive a multispectral dataset with each of the individual bands in separate files. You can also use layer stack to analyze datasets taken during different seasons when different sets show different stages for vegetation in an area.An example of a multispectral dataset with individual bands in separate files is Landsat TM data. Layer stack quickly consolidates the bands of data into one file.The image on this page is an example of a layer stack output. The files used are from the Amazon, and the red and blue bands are from one image, while the green band is from the other. Bands 1 and 3 are taken from the Amazon LBAND image, and the remaining layers taken from Amazon TM.

Figure 41: Stacked Image

144 Using Utilities

Using Layer Stack To use layer stack, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Utilities, and then select Layer Stack to open the Layer Stack dialog.

2. Click the browse button for the Input Images field and navigate to a directory where the input images are stored.

3. Check the check boxes for the layers of the input images you want to use.

4. Click the up and down arrows to the right of the Selected Layers box if you want to reorder the layers.


6. Click OK to close the Layer Stack dialog.

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Using Utilities 145

Rescale Image Rescale image has a tool that lets you rescale the pixel values of an input image into a new user-specified range for the specified output image. It lets you change the data type of the output image. For example, you can rescale a 16-bit image with an original min-max of 0-65535 into an 8-bit image of 0-255 and vice versa. You can access the Rescale tool by selecting Utilities/Rescale Image from the Image Analysis dropdown list.During conversion, the Rescale tool recomputes statistics for the input image and scales the minimum and maximum values obtained into the specified output values. A user-specified NoData value is also assigned. If a NoData value is not required, N/A is specified for the NoData value in the user interface (this is the same approach defined for the ImageInfo tool).You can use the Rescale tool by selecting an input image, and then selecting the output data type. This populates the New Min and New Max values to the minimum and maximum values appropriate for the selected data type. Define the output file name and click OK.Supported output data types are:

• Unsigned 1 bit

• Unsigned 2 bit

• Unsigned 4 bit

• Unsigned 8 bit

• Signed 8 bit

• Unsigned 16 bit

• Signed 16 bit

• Unsigned 32 bit

• Signed 32 bit

Optionally, you can select a NoData value. However, if you do not want to set a NoData value, leave it as N/A or blank in which case any previously defined NoData value is not transferred to the output image.A typical use for the Rescale tool is to rescale an unsigned 8-bit dataset with valid values ranging from 0-255 into a range of 1-255, and at the same time, set the NoData value to 0. This provides a means of allocating a NoData value that does not interfere with valid data values. One disadvantage of this technique for setting NoData is that pixel values are altered, which might not be good for certain applications such as classification.

146 Using Utilities

Using Rescale Image To use rescale image, follow these steps:

1. Add the image you want to rescale to your view.

2. Click the Image Analysis dropdown arrow, point to Utilities, and then select Rescale Image to open the Rescale Image dialog.

3. Select an input image from the Input Image dropdown list, or navigate to the directory where it is stored.

4. Select an output type from the Output Type dropdown list.

5. Type an output minimum and maximum in the Output Min and Output Max fields.

6. Type a value in the NoData Value field if you want to specify a NoData value.


8. Click OK to close the Rescale Image dialog.

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147Understanding Classification

Understanding Classification 147

Understanding ClassificationMultispectral classification is the process of sorting pixels into a finite number of individual classes, or categories of data, based on their data file values. If a pixel satisfies a certain set of criteria, the pixel is assigned to the class that corresponds to that criteria.Depending on the type of information you want to extract from the original data, classes can be associated with known features on the ground or represent areas that look different to the computer. An example of a classified image is a land cover map that shows vegetation, bare land, pasture, urban, and so on. This chapter covers the two ways to classify pixels into different categories:

• Unsupervised Classification

• Supervised Classification

Supervised classification is more closely controlled by you than unsupervised classification.

IN THIS CHAPTER

• The Classification Process

• Classification Tips

• Unsupervised Classification

• Supervised Classification

• Classification Decision Rules

148 Understanding Classification

The Classification Process

Pattern recognition is the science—and art—of finding meaningful patterns in data, which you can extract through classification. By spatially and spectrally enhancing an image, pattern recognition is performed with the human eye; the human brain automatically sorts certain textures and colors into categories.In a computer system, spectral pattern recognition is more scientific. Statistics are derived from the spectral characteristics of all pixels in an image. However, in supervised classification, the statistics are derived from the training samples, and not the entire image. After the statistics are derived, pixels are sorted based on mathematical criteria. The classification process breaks down into two parts: training and classifying (using a decision rule).

Training First, the computer system must be trained to recognize patterns in the data. Training is the process of defining the criteria by which these patterns are recognized (Hord 1982). You can perform training with either a supervised or an unsupervised method, as explained in the sections that follow.

Unsupervised Training Unsupervised training is more computer-automated. It lets you specify some parameters that the computer uses to uncover statistical patterns that are inherent in the data. These patterns do not necessarily correspond to directly meaningful characteristics of the scene, such as contiguous, easily recognized areas of a particular soil type or land use. They are basically clusters of pixels with similar spectral characteristics. In some cases, it might be more important to identify groups of pixels with similar spectral characteristics than it is to sort pixels into recognizable categories.Unsupervised training is dependent upon the data itself for the definition of classes. This method is usually used when less is known about the data before classification. It is then the analyst’s responsibility, after classification, to attach meaning to the resulting classes (Jensen 1996). Unsupervised classification is useful only if you can appropriately interpret the classes.

Supervised Training Supervised training is closely controlled by the analyst. In this process, select pixels that represent patterns or land cover features that you recognize, or that you can identify with help from other sources, such as aerial photos, ground truth data, or maps. Knowledge of the data, and of the classes you want, is required before classification.By identifying patterns and building a signature file, you can instruct the computer system to identify pixels with similar characteristics. If the classification is accurate, the resulting classes represent the categories in the data that you originally identified.


Signatures The result of training is a set of signatures that defines a training sample or cluster. Each signature corresponds to a class, and is used with a decision rule (explained below) to assign the pixels in the image file to a class. Signatures contain both parametric class definitions (mean and covariance) and non-parametric class definitions (parallelepiped boundaries that are the per band minima and maxima).A parametric signature is based on statistical parameters (for example, mean and covariance matrix) of the pixels that are in the training sample or cluster. Supervised and unsupervised training can generate parametric signatures. You can use a set of parametric signatures to train a statistically based classifier (that is, maximum likelihood) to define the classes.

Decision Rule After the signatures are defined, the pixels of the image are sorted into classes based on the signatures by use of a classification decision rule. The decision rule is a mathematical algorithm that, using data contained in the signature, performs the actual sorting of pixels into distinct class values.

Parametric Decision Rule A parametric decision rule is trained by the parametric signatures. These signatures are defined by the mean vector and covariance matrix for the data file values of the pixels in the signatures. When a parametric decision rule is used, every pixel is assigned to a class since the parametric decision space is continuous (Kloer 1994). There are three parametric decision rules offered:

• Minimum Distance

• Mahalanobis Distance

• Maximum Likelihood

Nonparametric Decision Rule

When a nonparametric rule is set, the pixel is tested against all signatures with nonparametric definitions. This rule results in the following conditions:

• If the nonparametric test results in one unique class, the pixel is assigned to that class.

• If the nonparametric test results in zero classes (for example, the pixel lies outside all the nonparametric decision boundaries), the pixel is assigned to a class called Unclassified.

Parallelepiped is the only nonparametric decision rule in Image Analysis for ArcGIS.

Classification Tips This section contains information about general classifications that can help you in processing data.


Classification Scheme Usually, classification is performed with a set of target classes in mind. Such a set is called a classification scheme (or classification system). The purpose of such a scheme is to provide a framework for organizing and categorizing the information that can be extracted from the data (Jensen 1983). The proper classification scheme includes classes that are both important to the study and discernible from the data on hand. Most schemes have a hierarchical structure, which can describe a study area in several levels of detail.A number of classification schemes have been developed by specialists who inventoried a geographic region. Some references for professionally developed schemes are listed below:

• Anderson, J. R., et al. 1976. “A Land Use and Land Cover Classification System for Use with Remote Sensor Data.” U.S. Geological Survey Professional Paper 964.

• Cowardin, Lewis M., et al. 1979. Classification of Wetlands and Deepwater Habitats of the United States. Washington, D.C.: U.S. Fish and Wildlife Service.

• Florida Topographic Bureau, Thematic Mapping Section. 1985. Florida Land Use, Cover and Forms Classification System. Florida Department of Transportation, Procedure No. 550-010-001-a.

• Michigan Land Use Classification and Reference Committee. 1975. Michigan Land Cover/Use Classification System. Lansing, Michigan: State of Michigan Office of Land Use.

Other states or government agencies may also have specialized land use or cover studies.It is recommended that you begin the classification process by defining a classification scheme for the application using previously developed schemes like those above as a general framework.


Supervised versus Unsupervised Classification

In supervised training, it is important to have a set of classes you want in mind, and then create the appropriate signatures from the data. You must also have some way of recognizing pixels that represent the classes you want to extract. Supervised classification is usually appropriate when you are identifying relatively few classes, when there are selected training sites you can verify with ground truth data, or when you can identify distinct, homogeneous regions that represent each class. In Image Analysis for ArcGIS, choose supervised classification if you need to correctly classify small areas with actual representation.If you want the classes determined by spectral distinctions inherent in the data so that you can define the classes later, the application is better suited to unsupervised classification. This lets you define many classes easily, and identify classes that are not in contiguous, easily recognized regions. If you have areas that have a value of zero, and you do not classify them as NoData (see “Applying Data Tools” on page 51), they are assigned to the first class when performing unsupervised classification. You can assign a specific class by taking a training sample when performing supervised classification.

Classifying Enhanced Data

For many specialized applications, classifying data that has been spectrally merged or enhanced—with principal components, image algebra, or other transformations—can produce very specific and meaningful results. However, without understanding the data and the enhancements used, it is recommended that you classify only the original, remotely sensed data.

Limiting Dimensions Although Image Analysis for ArcGIS lets you use an unlimited number of layers of data for one classification, it is usually wise to reduce the dimensionality of the data as much as possible. Often, certain layers of data are redundant or extraneous to the task at hand. Unnecessary data takes up valuable disk space, and causes the computer system to perform more arduous calculations, which slows down processing.


Unsupervised Classification

Unsupervised classification requires only minimal initial input from you. However, you must interpret the classes that are created by the unsupervised classification algorithm. Unsupervised classification is also called clustering, because it is based on the natural groupings of pixels in image data when they are plotted in feature space.If you need to classify small areas with small representation, use supervised classification. Due to the skip factor of 8 used by the unsupervised classification signature collection, small areas such as wetlands, small urban areas, or grasses can be wrongly classified on rural data sets.

Clusters Clusters are defined with a clustering algorithm, which often uses all or many of the pixels in the input data file for its analysis. The clustering algorithm has no regard for the contiguity of the pixels that define each cluster.The Iterative Self-Organizing Data Analysis Technique (ISODATA) (Tou and Gonzalez 1974) clustering method uses spectral distance as in the sequential method. But it iteratively classifies the pixels, redefines the criteria for each class, and classifies again, so that the spectral distance patterns in the data gradually emerge.

ISODATA Clustering ISODATA is iterative in that it repeatedly performs an entire classification (producing a thematic raster layer) and recalculates statistics. Self-organizing refers to the way in which it locates clusters with minimum user input.The ISODATA method uses minimum spectral distance to assign a cluster for each candidate pixel. The process begins with a specified number of arbitrary cluster means or the means of existing signatures. Then, it processes repetitively, so that those means shift to the means of the clusters in the data.Because the ISODATA method is iterative, it is not biased to the top of the data file, as are the one-pass clustering algorithms.


Initial cluster Means On the first iteration of the ISODATA algorithm, you can arbitrarily determine the means of N clusters. After each iteration, a new mean for each cluster is calculated based on the actual spectral locations of the pixels in the cluster instead of the initial arbitrary calculation. These new means are used for defining clusters in the next iteration. The process continues until there is little change between iterations (Swain 1973). The initial cluster means are distributed in feature space along a vector that runs between the point at spectral coordinates (µ1-σ1, µ2-σ2, µ3-σ3, ... µn-σn) and the coordinates (µ1+σ1, µ2+σ2, µ3+σ3, ... µn+σn). Such a vector in two dimensions is illustrated below. The initial cluster means are evenly distributed between (µA-σA, µB-σB) and (µA+σA, µB+σB).

Pixel Analysis Pixels are analyzed beginning with the upper-left corner of the image and going left to right, block-by-block. The spectral distance between the candidate pixel and each cluster mean is calculated. The pixel is assigned to the cluster whose mean is the closest. The ISODATA function creates an output image file with a thematic raster layer as a result of the clustering. At the end of each iteration, an image file shows the assignments of the pixels to the clusters.

ISODATA Arbitrary ClustersFive arbitrary cluster means in two-dimensional spectral space

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Considering the regular, arbitrary assignment of the initial cluster means, the first iteration of the ISODATA algorithm always gives results similar to those in this illustration.

For the second iteration, the means of all clusters are recalculated, causing them to shift in feature space. The entire process is repeated—each candidate pixel is compared to the new cluster means and assigned to the closest cluster mean.

Percentage Unchanged After each iteration, the normalized percentage of pixels whose assignments are unchanged since the last iteration displays in the dialog. When this number reaches T (the convergence threshold), the program terminates.

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Performing Unsupervised Classification

To perform unsupervised classification, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Classification, and then select Unsupervised/Categorize to open the Unsupervised Classification dialog.

2. Click the browse button for the Input Image field and navigate to the directory where the input file is stored.

3. Type the number of classes you want in the Desired Number of Classes field.


5. Click OK to close the Unsupervised Classification dialog.

Supervised Classification

Supervised classification requires a priori (already known) information about the data, such as:

• What type of classes need to be extracted? Soil type? Land use? Vegetation?

• What classes are most likely to be present in the data? That is, which types of land cover, soil, or vegetation (or whatever) are represented by the data?

In supervised training, you rely on your own pattern recognition skills and a priori knowledge of the data to help the system determine the statistical criteria (signatures) for data classification. You should know some information—either spatial or spectral—about the pixels that you want to classify to select reliable samples.

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The location of a specific characteristic, such as a land cover type, may be known through ground truthing. Ground truthing refers to the acquisition of knowledge about the study area from field work, analysis of aerial photography, personal experience, and so on. Ground truth data is considered to be the most accurate (true) data available about the area of study. It should be collected at the same time as the remotely sensed data, so that the data corresponds as much as possible (Star and Estes 1990). However, some ground data may not be very accurate due to a number of errors and inaccuracies.

Performing Supervised Classification

To perform supervised classification, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Classification, and then select Supervised to open the Supervised Classification dialog.

2. Click the browse button for the Input Image field and navigate to the directory where the file is stored.

3. Click the browse button for the Signature Features field and navigate to the directory where the file is stored.

4. Select the field that contains the class names from the Class Name Field dropdown list.

5. Click either the All Features or Selected Features button to specify which features to use during classification.

6. Select the rule you want to use from the Classification Rule dropdown list.Note: For more information about each option, see Classification Decision Rules on page 157.

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8. Click OK to close the Supervised Classification dialog.

Classification Decision Rules

Once you create and evaluate a set of reliable signatures, the next step is to perform a classification of the data. Each pixel is analyzed independently. The measurement vector for each pixel is compared to each signature according to a decision rule or algorithm. Pixels that pass the criteria and are established by the decision rule are then assigned to the class for that signature. Image Analysis for ArcGIS lets you classify the data parametrically with statistical representation.

Parametric Rules Image Analysis for ArcGIS provides these commonly used decision rules for parametric signatures:

• Minimum Distance

• Mahalanobis Distance

• Maximum Likelihood (With Bayesian Variation)

Nonparametric Rule Image Analysis for ArcGIS provides only one decision rule for nonparametric signatures:

• Parallelepiped

Minimum Distance The minimum distance decision rule (also called spectral distance) calculates the spectral distance between the measurement vector for the candidate pixel and the mean vector for each signature.

In this illustration, spectral distance is illustrated by the lines from the candidate pixel to the means of the three signatures. The candidate pixel is assigned to the class with the closest mean.

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The equation for classifying by spectral distance is based on the equation for Euclidean distance.

Where:n =Number of bands (dimensions)i = A particular bandc = A particular classXxyi = Data file value of pixel x,y in band iµci =Mean of data file values in band i for the sample for class cSDxyc = Spectral distance from pixel x,y to the mean of class c

Source: Swain and Davis 1978When spectral distance is computed for all possible values of c (all possible classes) the class of the candidate pixel is assigned to the class for which SD is the lowest.

Maximum Likelihood The Maximum Likelihood decision rule is based on the probability that a pixel belongs to a particular class. The basic equation assumes that these probabilities are equal for all classes, and that the input bands have normal distributions.Note: The Maximum Likelihood algorithm assumes that the histograms of the bands of data have normal distributions. If this is not the case, you might have better results with the minimum distance decision rule.The equation for the Maximum Likelihood/Bayesian Classifier is as follows:

Where:D = Weighted distance (likelihood)c =A particular classX = The measurement vector of the candidate pixelMc = The mean vector of the sample of class cac = Percent probability that any candidate pixel is a member of class c (defaults to 1.0, or is entered from a priori data)Covc = The covariance matrix of the pixels in the sample of class c|Covc|=Determinant of Covc (matrix algebra)

SDxyc µci Xxyi–( )2

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ac( ) 0.5 Covc( )ln[ ]– 0.5 X Mc–( )T Covc1–

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Covc-1 = Inverse of Covc (matrix algebra)ln = Natural logarithm functionT = Transposition function (matrix algebra)

Mahalanobis Distance Mahalanobis distance is similar to minimum distance, except that the covariance matrix is used in the equation. Variance and covariance are figured in so that clusters that are highly varied lead to similarly varied classes, and vice versa. For example, when classifying urban areas—typically a class whose pixels vary widely—correctly classified pixels may be farther from the mean than those of a class for water, which is usually not a highly varied class (Swain and Davis 1978). Note: The Mahalanobis distance algorithm assumes that the histograms of the bands have normal distributions. If this is not the case, you might have better results with the parallelepiped or minimum distance decision rule, or by performing a first-pass parallelepiped classification.The equation for the Mahalanobis distance classifier is as follows:

Where:D= Mahalanobis distancec= A particular classX= The measurement vector of the candidate pixelMc= The mean vector of the signature of class cCovc= The covariance matrix of the pixels in the signature of class cCovc

-1= Inverse of CovcT= Transposition function

The pixel is assigned to the class, c, for which D is the lowest.

Parallelepiped Image Analysis for ArcGIS provides the Parallelepiped decision rule as its nonparametric decision rule. In the Parallelepiped decision rule, the data file values of the candidate pixel are compared to upper and lower limits, which are the minimum and maximum data file values of each band in the signature.There are high and low limits for every signature in every band. When a pixel’s data file values are between the limits for every band in a signature, the pixel is assigned to that signature’s class. If a pixel falls into more than one class, the first class is the one assigned. If a pixel falls into no class boundaries, it is labeled unclassified.

D X Mc–( )T Covc

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147Using Conversion

Using Conversion 147

Using Conversion The conversion feature gives you the ability to convert shapefiles to raster images and raster images to shapefiles. This tool is very helpful when you need to isolate or highlight certain parts of a raster image or when you have a shapefile and you need to view it as a raster image. Possible applications include viewing deforestation patterns, urban sprawl, and shore erosion.The Image Info tool that is discussed in “Applying Data Tools” on page 51 is also an important part of raster/feature conversion. The ability to assign certain pixel values as NoData is very helpful when converting images.

IN THIS CHAPTER

• Conversion

• Converting Raster to Features

• Converting Features to Raster

148 Using Conversion

Conversion Always be aware of how the raster dataset represents features when converting points, polygons, or polylines to a raster, and vice versa. There is a trade-off when working with a cell-based system. Although points don't have area, cells do even if points are represented by a single cell. The smaller the cell size, the smaller the area, and thus a closer representation of the point feature.Points with area have an accuracy of plus or minus half the cell size. For many users, having all data types in the same format and being able to use them interchangeably in the same language is more important than a loss of accuracy.Linear data is represented by a polyline that is also comprised of cells. Therefore, it has area although, by definition, lines do not. Because of this, the accuracy of representation varies according to the scale of the data and the resolution of the raster dataset.With polygonal or areal data, problems can occur from trying to represent smooth polygon boundaries with square cells. The accuracy of the representation is dependent upon the scale of the data and the size of the cell. The finer the cell resolution and the greater the number of cells that represent small areas, the more accurate the representation.

Converting Raster to Features

During a conversion of a raster representing polygonal features to a polygonal vector file or feature dataset, the polygons are built from groups of contiguous cells having the same cell values. Arcs are created from cell borders in the raster. Continuous cells with the same value are grouped together to form polygons. Cells that are NoData in the input raster do not become features in the output polygon feature.When a raster that represents linear features is converted to a polyline vector file or feature dataset, a polyline is created from each cell in the input raster, passing through the center of each cell. Cells that are NoData in the input raster do not become features in the output polyline feature.When you convert a raster representing point features to point vector file or feature dataset, a point is created in the output for each cell of the input raster. Each point is positioned at the center of the cell it represents. NoData cells are not transformed into points.When you choose Convert Raster to Feature, the Raster to Feature dialog gives you the choice of a field to specify from the image in the conversion. You are also given the choice of an output geometry type so you can specify whether the feature is a point, a polygon, or a polyline according to the field and data you’re using. You can specify Generalize Lines to smooth out ragged or sharp edges in the new feature file. You should note that regardless of which field you select, the category is not populated in the Attribute table after conversion.The images below show a raster image before conversion and a raster image after conversion to a shapefile using Value as the field.


Figure 42: Raster Image before Conversion

Figure 43: Raster Image after Conversion

Performing Raster to Features Conversion

To perform raster to features conversion, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Convert, and then select Convert Raster to Features to open the Raster to Features dialog.


2. Click the browse button for the Input Raster field and navigate to the directory where the file is stored.

3. Select a field to use from the Field dropdown list.

4. Select Point, Polygon, or Polyline from the Output Geometry Type dropdown list.

5. Check the Generalize Lines check box if you want to smooth out sharp edges in the image.

6. Type the file name of the shapefile in the Output Features field, or navigate to the directory where you want it stored.

7. Click OK to close the Raster to Features dialog.

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Converting Features to Raster

You can convert polygons, polylines, or points from any source file to a raster. You can convert features using both string and numeric fields. Each unique string in a string field is assigned a unique value to the output raster. A field is added to the table of the output raster to hold the original string value from the features.When you convert points, cells are given the value of the points found within each cell. Cells that do not contain a point are given the value of NoData. You can specify a cell size to use in the Features to Raster dialog. Specify a cell size based on these factors:

• The resolution of the input data

• The output resolution needed to perform your analysis

• The need to maintain a rapid processing speed

Polylines are features that, at certain resolutions, appear as lines representing streams or roads. When you convert polylines, cells are given the value of the line that intersects each cell. Cells that are not intersected by a line are given the value NoData. If more than one line is found in a cell, the cell is given the value of the first line encountered while processing. Using a smaller cell size during conversion alleviates this.Polygons are used for buildings, forests, fields, and many other features that are best represented by a series of connected cells. When you convert polygons, the cells are given the value of the polygon found at the center of each cell.


Performing Features to Raster Conversion

To perform features to raster conversion, follow these steps:

1. Click the Image Analysis dropdown arrow, point to Convert, and then select Convert Features to Raster to open the Features to Raster dialog.

2. Click the browse button for the Input features field and navigate to the directory where the file is stored.

3. Select a field to use from the Field dropdown list.

4. Type an output cell size in the Output Cell Size field.


6. Click OK to close the Features to Raster dialog.

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153Applying GeoCorrection Tools

Applying GeoCorrection Tools 153

Applying GeoCorrection ToolsThe tools and methods in this chapter describe the process of geometrically correcting the distortions in images caused by sensors and the curvature of the Earth. Even images of seemingly flat areas are distorted, but you can correct, or rectify, these images so they are represented on a planar surface, conform to other images, and have the integrity of a map.The terms geocorrection and rectification are used synonymously when discussing geometric correction. Rectification is the process of transforming data from one grid system into another grid system using a geometric transformation. Because the pixels of a new grid may not align with the pixels of the original grid, you must resample the pixels. Resampling is the process of extrapolating data values for the pixels on the new grid from the values of the source pixels.Orthorectification is a form of rectification that corrects for terrain displacement and is used if there is a DEM of the study area. It is based on collinearity equations, which is derived by using 3D Ground GCPs. In relatively flat areas, orthorectification is not necessary, but is recommended in mountainous areas (or on aerial photographs of buildings) where a high degree of accuracy is required.IN THIS CHAPTER

• Rectification

• GeoCorrection

• SPOT

• Polynomial Transformation

• Rubber Sheeting

• Camera

• IKONOS, QuickBird, and RPC Properties

• Landsat

154 Applying GeoCorrection Tools

Rectification Rectification is necessary in cases where you must change the pixel grid of an image to fit a map projection system or a reference image. There are several reasons for rectifying image data:

• Comparing pixels scene-to-scene in applications, such as change detection or thermal inertia

• Mapping (day and night comparison)

• Developing GIS databases for GIS modeling

• Identifying training samples according to map coordinates prior to classification

• Creating accurate, scaled photomaps

• Overlaying an image with vector data

• Comparing images that are originally at different scales

• Extracting accurate distance and area measurements

• Mosaicking images

• Performing any other analyses requiring precise geographic locations

Before rectifying the data, consider the primary use for the database before selecting the optimum map projection and appropriate coordinate system. If you are doing a government project, the projection is often predetermined. A commonly used projection in the United States government is state plane. Use an equal area projection for thematic or distribution maps and conformal or equal area projections for presentation maps. Consider the following before selecting a map projection:

• How large or small an area is mapped? Different projections are intended for different size areas.

• Where on the globe is the study area? Polar regions and equatorial regions require different projections for maximum accuracy.

• What is the extent of the study area? Circular, north-south, east-west, and oblique areas may all require different projection systems (ESRI 1992).


Disadvantages of Rectification

During rectification, you must resample the data file values of rectified pixels to fit into a new grid of pixel rows and columns. Although some of the algorithms for calculating these values are highly reliable, you can lose some spectral integrity of the data during rectification. If map coordinates or map units are not needed in the application, it might be wiser not to rectify the image. An unrectified image is more spectrally correct than a rectified image.

Georeferencing Georeferencing refers to the process of assigning map coordinates to image data. The image data might already project onto the plane, but not reference the proper coordinate system. Rectification, by definition, involves georeferencing, because all map projection systems are associated with map coordinates. Image-to-image registration involves georeferencing only if the reference image is already georeferenced. Georeferencing, by itself, involves changing only the map coordinate information in the image file. The grid of the image does not change.Geocoded data are images that are rectified to a particular map projection and pixel size, usually with radiometric corrections applied. It is possible to purchase image data that is already geocoded. You should rectify geocoded data only if it must conform to a different projection system or be registered to other rectified data.

Georeferencing Only Rectification is unnecessary if there is no distortion in the image. For example, if an image file is produced by scanning or digitizing a paper map that is in the projection system you want, the image is already planar and does not require rectification unless there is some skew or rotation of the image. Scanning or digitizing produces images that are planar, but do not contain any map coordinate information. You can georeference these images, which is a much simpler process than rectification. In many cases, you need only to update the image header with new map coordinate information. This involves redefining:

• The map coordinate of the upper-left corner of the image

• The cell size (the area represented by each pixel)

This information is usually the same for each layer of an image file, although it can be different. For example, the cell size of band 6 of Landsat TM data is different from the cell size of the other bands.

Ground Control Points GCPs are specific pixels in an image for which the output map coordinates (or other output coordinates) are known. GCPs consist of two X,Y pairs of coordinates:

• Source Coordinates – Usually data file coordinates in the image you are rectifying

• Reference Coordinates – The coordinates of the map or reference image to which the source image is being registered


The term map coordinates is sometimes used loosely to apply to reference coordinates and rectified coordinates. These coordinates are not limited to map coordinates. For example, map coordinates are unnecessary in image-to-image registration.

Entering GCP Coordinates

Accurate GCPs are essential for an accurate rectification. From the GCPs, the rectified coordinates for all other points in the image are extrapolated. Select many GCPs throughout the scene. The more dispersed the GCPs are, the more reliable the rectification. GCPs for large-scale imagery might include the intersection of two roads, airport runways, utility corridors, towers, or buildings. For small-scale imagery, you can use larger features such as urban areas or geologic features. Do not use landmarks that can vary (edges of lakes, other water bodies, vegetation, and so on).You can enter the source and reference coordinates of the GCPs in the following ways:

• Enter a known a priori at the keyboard.

• Use your mouse to select a pixel from an image in the view. With both the source and destination views open, enter source coordinates and reference coordinates for image-to-image registration.

• Use a digitizing tablet to register an image to a hardcopy map.

Tolerance of RMSE Acceptable RMSE is determined by the end use of the database, the type of data used, and the accuracy of the GCPs and ancillary data used. For example, GCPs acquired from GPS should have an accuracy of about 10 m, but GCPs from 1:24,000-scale maps should have an accuracy of about 20 m.It is important to remember that RMSE is reported in pixels. Therefore, if you are rectifying Landsat TM data and want the rectification accurate to within 30 meters, the RMSE should not exceed 1.00. Acceptable accuracy depends on the image area and the project.

Classification Some analysts recommend classification before rectification because the classification is then based on the original data values. Another benefit is that a thematic file has only one band to rectify instead of the multiple bands of a continuous file. On the other hand, it might benefit you to rectify the data first, especially when using GPS data for the GCPs. Because this data is very accurate, the classification might be more accurate if the new coordinates help to locate better training samples.

Thematic Files Nearest neighbor is the only appropriate resampling method for thematic files, which is a drawback in some applications. The available resampling methods are discussed in detail in Options Dialog on page 62.


Orthorectification Orthorectification is a form of rectification that corrects for terrain displacement and is used if there is a DEM of the study area. It is based on collinearity equations, which is derived by using 3D GCPs. In relatively flat areas, orthorectification is not necessary, but in mountainous areas (or on aerial photographs of building), where a high degree of accuracy is required, orthorectification is recommended.

GeoCorrection You can perform georeferencing, which is the process of assigning map coordinates to image data by using various sensor models, using the GeoCorrection properties dialogs and the Add Links tool.

The GeoCorrection Properties Dialog

The individual GeoCorrection tools have their own dialog. It displays whenever you select a model type for an image on the Image Analysis toolbar and click the GeoCorrection Properties button. Some of the dialogs for these tools contain tabs pertaining to that specific tool, but they all have several tabs in common. Every GeoCorrection tool dialog has a General tab and a Links tab, and all but Polynomial properties and Rubber Sheeting properties have an Elevation tab.

General Tab The General tab has the following settings:

• Link Coloring – Lets you set a threshold and select or change link colors.

• Displayed Units – Lets you view the horizontal and vertical units if they are known. Often only one is known so it might display Meters for vertical units and Unknown for horizontal units. Display units do not have an effect on the original data in latitude/longitude format. The image in the view does not show the changes either.

To specify settings on the General tab, follow these steps:

1. Type a number in the Threshold field, and then click the Within Threshold and Over Threshold color bar arrows to change the link colors.

2. View the measurement of the vertical units in the Displayed Units box.

Links Tab The Links tab (this display is also called a CellArray) displays information about the links in your image, including reference points and RMSE. If you added links to your image, they are listed on this tab. The program is interactive between the image and the Links tab, so when you add links in an image or between two images, information is automatically updated in the CellArray. You can edit and delete information displayed in the CellArray. For example, if you want to experiment with coordinates other than the ones you’ve been given, you can plug your own coordinates into the CellArray.


Note: Before adding links or editing the links table, you must select the coordinate system in which you want to store the link coordinates.To select a coordinate system, follow these steps:

1. Right-click in the view and select Data Frame Properties to open the Data Frame Properties dialog.

2. Click the Coordinate System tab.

3. Select the appropriate predefined coordinate system in the Select a Coordinate System box if your link coordinates are predefined. Note: Expand the Layers folder and select that layer if you want to use the coordinate system from a specific layer.

4. Click OK to close the Data Frame Properties dialog.

3

4

2


To perform a few additional checks you need to make before proceeding, follow these steps:

1. Make sure that the correct layer displays in the Layers dropdown list on the Image Analysis toolbar.

2. Select your model type from the Model Types dropdown list.

3. Click the Add Links button to set your new links.To proof and edit the coordinates of the links as you enter them, follow these steps:

1. Click the GeoCorrection Properties button.

2. Click the Links tab. The coordinates display in the CellArray on this tab.

3. Click in a cell and edit the contents.

4. Click the Export Links to Shapefile button and save the new shapefile.

1 2

3

2 3

4


Elevation Tab The Elevation tab is in all GeoCorrection Model properties dialogs except for Polynomial and Rubber Sheeting. The default settings on the Elevation tab let you choose a file to use as an elevation source.

Figure 44: Elevation Source File Settings

If you do not have an elevation file, click the Constant button to change the settings in the Elevation Source box and specify the elevation value and units. Use the constant value that is the average ground elevation for the entire scene.

Figure 45: Elevation Source Constant Settings

Note: You can also check the Account for Earth’s curvature check box if you want to use this option as part of the elevation.


The following steps take you through the Elevation tab. The first set of instructions uses File as the elevation source. The second set uses Constant as the elevation source.To use a file value as the elevation source, follow these steps:

1. Click the File button in the Elevation Source box.

2. Type the name of the file in the Elevation File field, or navigate to the directory where it is stored.

3. Select Feet or Meters from the Elevation Units dropdown list.


5. Click Apply to set the elevation source.

6. Click OK to close the dialog.To use a constant value as the elevation source, follow these steps:

1. Click the Constant button in the Elevation Source box.

2. Type the elevation value in the Elevation Value field.

3. Select Feet or Meters from the Elevation Units dropdown list.


5. Click Apply to set the elevation source.

6. Click OK to close the dialog.

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2

3

4

1

4

2

3


SPOT The first SPOT satellite, developed by the French Centre National d’Etudes Spatiales (CNES), was launched in early 1986. The second SPOT satellite was launched in 1990, and the third was launched in 1993. The sensors operate in two modes, multispectral and panchromatic. SPOT is commonly referred to as a pushbroom scanner, which means that all scanning parts are fixed, and scanning is accomplished by the forward motion of the scanner. SPOT pushes 3000/6000 sensors along its orbit. This is different from Landsat, which scans using 16 detectors perpendicular to its orbit.The SPOT satellite can observe the same area on the globe once every 26 days. The SPOT scanner normally produces nadir views, but it does have off-nadir viewing capability. Off-nadir refers to any point that is not directly beneath the detectors, but off to an angle. Using this off-nadir capability, you can view one area on the Earth as often as every three days.Off-nadir viewing is programmable from the ground control station. It is useful for collecting data in a region not directly in the path of the scanner, or where timeliness of data acquisition is crucial in the event of a natural or man-made disaster. It is also very useful in collecting stereo data from which you can extract elevation data. The width of the swath observed varies between 60 km for nadir viewing and 80 km for off-nadir viewing at a height of 832 km (Jensen 1996).

Panchromatic SPOT Panchromatic (meaning sensitive to all visible colors) has 10 × 10 m spatial resolution, contains 1 band—0.51 to 0.73 mm—and is similar to a black-and-white photograph. It has a radiometric resolution of 8 bits (Jensen 1996).


XS SPOT XS, or multispectral, has 20 × 20 m spatial resolution, 8-bit radiometric resolution, and contains 3 bands (Jensen 1996).

Table 6: SPOT XS Bands and Wavelengths

Figure 46: SPOT Panchromatic versus SPOT XS

Stereoscopic Pairs The panchromatic scanner can make two observations on successive days, so that the two images are acquired at angles on either side of the vertical, resulting in stereoscopic imagery. Stereoscopic imagery is also achieved by using one vertical scene and one off-nadir scene. This type of imagery can be used to produce a single image, or topographic and planimetric maps (Jensen 1996). Topographic maps indicate elevation. Planimetric maps correctly represent horizontal distances between objects (Star and Estes 1990).

Band Wavelength(Microns)

Comments

1, Green 0.50 to 0.59 µm

This band corresponds to the green reflectance of healthy vegetation.

2, Red 0.61 to 0.68 µm

This band is useful for discriminating between plant species. It is also useful for soil boundary and geological boundary delineations.

3, Reflective IR

0.79 to 0.89 µm

This band is especially responsive to the amount of vegetation biomass present in a scene. It is useful for crop identification and emphasizes soil/crop and land/water contrasts.

Panchromatic

XS

1 Band

3 Bands

1 Pixel =10 m x 10 m


RadiometricResolution

0-255


SPOT 4 The SPOT 4 satellite was launched in 1998. SPOT 4 carries High Resolution Visible Infrared (HR VIR) instruments that obtain information in the visible and near-infrared spectral bands. It orbits the Earth at 822 km above the Equator and has two sensors on board: a multispectral sensor, and a panchromatic sensor. The multispectral scanner has a pixel size of 20 × 20 m, and a swath width of 60 km. The panchromatic scanner has a pixel size of 10 × 10 m, and a swath width of 60 km.

Table 7: SPOT 4 Bands and Wavelengths

The Spot Properties Dialog Box

In addition to the General, Links, and Elevation tabs, the Spot Properties dialog also contains a Parameters tab. Most of the GeoCorrection Properties dialogs contain a Parameters tab, but each one offers different options.To specify settings on the Parameters tab on the Spot Properties dialog, follow these steps:

1. Select Spot from the Model Types dropdown list on the Image Analysis toolbar.

2. Click the GeoCorrection Properties button to open the Spot Properties dialog.

Band Wavelength

1, Green 0.50 to 0.59 µm

2, Red 0.61 to 0.68 µm

3, (near-IR) 0.78 to 0.89 µm

4, (mid-IR) 1.58 to 1.75 µm

Panchromatic 0.61 to 0.68 µm


3. Click the Parameters tab.

4. Click the XS/XI or Pan button to specify the sensor type.

5. Type the number of iterations in the Number of Iterations field.

6. Type a number for the incidence angle in the Incidence Angle field.

7. Type the pixel value, which forms the background, in the Value field.

8. Type the number of the layer in the Layer field.

9. Click OK to close the Spot Properties dialog.

Polynomial Transformation

Polynomial equations are used to convert source file coordinates to rectified map coordinates. Depending on the distortion in the imagery, complex polynomial equations might be required to express the needed transformation. The degree of complexity of the polynomial is expressed as the order of the polynomial. The order of transformation is the order of the polynomial used in the transformation. Image Analysis for ArcGIS allows 1st through nth order transformations. Usually, 1st or 2nd order transformations are used.

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7

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3

9

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8


Transformation Matrix A transformation matrix is computed from the GCPs. The matrix consists of coefficients that are used in polynomial equations to convert the coordinates. The size of the matrix depends on the order of transformation. The goal in calculating the coefficients of the transformation matrix is to derive the polynomial equations for which there is the least possible amount of error when used to transform the reference coordinates of the GCPs into the source coordinates. It is not always possible to derive coefficients that produce no error. For example, in the figure below, GCPs are plotted on a graph and compared to the curve that is expressed by a polynomial.

Every GCP influences the coefficients, even if there isn’t a perfect fit of each GCP to the polynomial that the coefficients represent. The distance between the GCP reference coordinate and the curve is called RMSE, which is discussed later in Camera on page 177.

Linear Transformations A 1st order transformation is a linear transformation. It can change:

• Location in X or Y

• Scale in X or Y

• Skew in X or Y

• Rotation

You can use 1st order transformations to project raw imagery to a planar map projection, convert a planar map projection to another planar map projection, and rectify relatively small image areas. You can perform simple linear transformations to an image in a view or to the transformation matrix itself. Linear transformations may be required before collecting GCPs for the displayed image. You can reorient skewed Landsat TM data, rotate scanned quad sheets according to the angle of declination stated in the legend, and rotate descending data so that north is up.

Source X CoordinateR

efer

ence

X C

oord

inat

e

GCP

Polynomial Curve


You can also use a 1st order transformation for data already projected onto a plane. For example, SPOT and Landsat Level 1B data is already transformed to a plane, but might not be rectified to the map projection you want. When doing this type of rectification, it is not advisable to increase the order of transformation if a high RMSE occurs first. Examine other factors, such as the GCP source and distribution, and then look for systematic errors.The transformation matrix for a 1st order transformation consists of six coefficients—three for each coordinate (X and Y):

Coefficients are used in a 1st order polynomial as follows:

Where:x and y are source coordinates (input)x0 and y0 are rectified coordinates (output)

The coefficients of the transformation matrix are as above.

Nonlinear Transformations

You can use 2nd order transformations to convert Lat/Lon data to a planar projection, for data covering a large area (to account for the Earth’s curvature), and with distorted data (for example, due to camera lens distortion). Use 3rd order transformations with distorted aerial photographs, on scans of warped maps, and with radar imagery. You can use 4th order transformations on very distorted aerial photographs.The transformation matrix for a transformation of order t contains this number of coefficients:

It is multiplied by two for the two sets of coefficients—one set for X and one for Y.An easier way to arrive at the same number is:

Clearly, the size of the transformation matrix increases with the order of the transformation.

a0 a1 a2b0 b1 b2

x0 a0 a1x a2y+ +=

y0 b0 b1x b2y+ +=

2 i

i 0=

t 1+

∑

t 1+( )x t 2+( )


High-Order Polynomials The polynomial equations for a t order transformation take this form:

Where:t is the order of the polynomiala and b are coefficients

The subscript k in a and b is determined by:

Effects of Order The computation and output of a higher polynomial equation are more complex than a lower-order polynomial equation. Therefore, you should use higher-order polynomials to perform more complicated image rectifications. It is helpful to see the output of various orders of polynomials to understand the effects of different orders of transformation in image rectification.The following example uses only one coordinate (X) instead of the two (X,Y) used in the polynomials for rectification. This lets you draw two-dimensional graphs that illustrate the way higher orders of transformation affect the output image. Because only the X coordinate is used in these examples, the number of GCPs used is less than the number required to perform the different orders of transformation.Coefficients like those in this example are generally calculated by the least squares regression method. Suppose there are GCPs with the following X coordinates:

These GCPs allow a 1st order transformation of the X coordinates, which is satisfied by this equation (the coefficients are in parentheses):

xo

tΣ

i o= i

Σj o=

= ak xi j–¥ yj¥

yo

tΣ

i o= i

Σj o=

= bk xi j–¥ yj¥

k i i j+Þ2

------------------ j+=

Source X Coordinate(Input)

Reference X Coordinate(Output)

1 17

2 9

3 1

xr 25( ) 8–( )xi+=


Where:xr = Reference X coordinatexi = Source X coordinate

This equation takes on the same format as the equation of a line (y = mx + b). In mathematical terms, a 1st order polynomial is linear. Therefore, a 1st order transformation is also known as a linear transformation. This equation is graphed below:

0 1 2 3 4

0

4

8

12

16

Ref

eren

ce X

Coo

rdin

ate

Source X Coordinate

xr = (25) + (-8)xi


However, what if the second GCP were changed as follows?

These points are plotted against each other below:

A line cannot connect these points, which illustrates why they are not expressed by a 1st order polynomial like the graph on the left. In this case, a 2nd order polynomial equation expresses these points.

Polynomials of the 2nd order or higher are nonlinear. The graph of this curve is drawn below:



1 17

2 7

3 1

0 1 2 3 4

0

4

8

12

16

Ref

eren

ce X

Coo

rdin

ate

Source X Coordinate

xr 31( ) 16–( )xi 2( )xi2+ +=

0 1 2 3 4

0

4

8

12

16

Ref

eren

ce X

Coo

rdin

ate

Source X Coordinate

xr = (31) + (-16)xi + (2)xi2


What if you added one more GCP to the list?

As illustrated in the graph above, this fourth GCP does not fit on the curve of the 2nd order polynomial equation. You must increase the order of the transformation to the 3rd order to ensure that all the GCPs fit. The equation and graph below are possible results:



1 17

2 7

3 1

4 5

0 1 2 3 4

0

4

8

12

16

Ref

eren

ce X

Coo

rdin

ate

Source X Coordinate

xr = (31) + (-16)xi + (2)xi2

(4,5)

xr 25( ) 5–( )xi 4–( )xi2 1( )xi

2+ + +=

0 1 2 3 4

0

4

8

12

16

Ref

eren

ce X

Coo

rdin

ate

Source X Coordinate

xr = (25) + (-5)xi + (-4)xi2 + (1)xi

3


The figure above illustrates a 3rd order transformation. However, this equation may be unnecessarily complex. Performing a coordinate transformation with this equation can cause unwanted distortions in the output image for the sake of a perfect fit for all GCPs. In this example, a 3rd order transformation probably is too high, because the output pixels in the X direction are arranged in a different order than the input pixels in the X direction.

In this case, a higher order of transformation probably does not produce the results you want.



1

2

3

4

x0 1( ) 17=

x0 2( ) 7=

x0 3( ) 1=

x0 4( ) 5=

x0 1( ) x0 2( ) x0 4( ) x0 3( )>>>

17 7 5 1>>>

1 2 3 4

1 2 3 4

Input ImageX Coordinates

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18

3 4 2 1

Output ImageX Coordinates


Minimum Number of GCPs

You can use higher orders of transformation to correct more complicated types of distortion. However, more GCPs are needed to use a higher order of transformation. For example, three points define a plane. Therefore, to perform a 1st order transformation, which is expressed by the equation of a plane, at least three GCPs are needed. Similarly, the equation in a 2nd order transformation is the equation of a paraboloid. Six points are required to define a paraboloid. Therefore, at least six GCPs are required to perform a 2nd order transformation. The minimum number of points required to perform a transformation of order t equals:

Use more than the minimum number of GCPs whenever possible. Although it is possible to get a perfect fit, it is rare, no matter how many GCPs are used.For 1st through 10th order transformations, the minimum number of GCPs required to perform a transformation is listed in this table:

Table 8: Number of GCPs

Order of Transformation

Minimum GCPs Required

1 3

2 6

3 10

4 15

5 21

6 28

7 36

8 45

9 55

10 66

t 1+( ) t 2+( )( )2

--------------------------------------


The Polynomial Properties Dialog Box

The Polynomial Properties dialog has a Parameters tab in addition to the General and Links tabs. It does not have an Elevation tab. The General tab and the Links tab are the same as the ones featured at the beginning of this chapter. The Parameters tab contains a CellArray that shows the transformation coefficients table. The table is populated when the model is solved.To specify settings on the Polynomial Properties Parameters tab, follow these steps:

1. Type the number for the polynomial order in the Polynomial Order field.

2. Click OK.

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Rubber Sheeting Triangle-based finite element analysis is a powerful tool for solving complicated computation problems that can be approached by small simpler pieces. It is widely used as a local interpolation technique in geographic applications. For image rectification, known control points can be triangulated into many triangles. Each triangle has three control points as its vertices. You can then use the polynomial transformation to establish mathematical relationships between source and destination systems for each triangle. Because the transformation passes through each control point and is not in a uniform manner, finite element analysis is also called rubber sheeting. You can also call it the triangle-based rectification because the transformation and resampling for image rectification are performed on a triangle-by-triangle basis. Use this triangle-based technique when other rectification methods such as polynomial transformation and photogrammetric modeling cannot produce acceptable results.

Triangulation It is necessary to triangulate the control points into a mesh of triangles to perform the triangle-based rectification. Watson (1994) summarily listed four kinds of triangulation, including the Arbitrary, Optimal, Greedy, and Delaunay triangulation. Of the four kinds, the Delaunay triangulation is most widely used and is adopted because of the smaller angle variations of the resulting triangles.You can construct the Delaunay triangulation by the empty circumcircle criterion. The circumcircle formed from three points of any triangle does not have any other points inside. The triangles defined this way are the most equiangular possible.

Triangle-Based Rectification

Once the triangle mesh is generated and the spatial order of the control points is available, you can perform the geometric rectification on a triangle-by-triangle basis. This triangle-based method is appealing because it breaks the region into smaller subsets. If the geometric problem of the region is very complicated, the geometry of each subset is much simpler and modeled through simple transformation.For each triangle, you can use the polynomials as the general transformation form between source and destination systems.

Linear Transformation The easiest and fastest transformation is the linear transformation with the 1st order polynomials:

Additional information is not necessary because there are three known conditions in each triangle and three unknown coefficients for each polynomial.

xo a0 a1x a2y+ +=

yo b0 b1x b2y+ +=


Nonlinear Transformation

Although linear transformation is easy and fast, it has one disadvantage in that the transitions between triangles are not always smooth. This phenomenon is obvious when shaded relief or contour lines are derived from the DEM that is generated by the linear rubber sheeting. It is caused by incorporating the slope change of the control data at the triangle edges and vertices. In order to distribute the slope change smoothly across triangles, the nonlinear transformation with polynomial order larger than 1 is used by considering the gradient information.The fifth order or quintic polynomial transformation is chosen as the nonlinear rubber sheeting technique in the example that follows. It is a smooth function. The transformation function and its first order partial derivative are continuous. It is not difficult to construct (Akima 1978).The formulation is as follows:

The 5th order has 21 coefficients for each polynomial to be determined. For solving these unknowns, 21 conditions are available. For each vertex of the triangle, one point value is given, and two 1st order and three 2nd order partial derivatives are easily derived by establishing a 2nd order polynomial using vertices in the neighborhood of the vertex. Then, the total 18 conditions are ready for use. You can obtain three more conditions by assuming that the normal partial derivative on each edge of the triangle is a cubic polynomial. This means that the sum of the polynomial items beyond the 3rd order in the normal partial derivative has a value of zero.

Checkpoint Analysis It should be emphasized that the independent checkpoint analysis is critical for determining the accuracy of rubber sheeting modeling. For an exact modeling method like rubber sheeting, the GCPs, which are used in the modeling process, do not have much geometric residuals remaining. The accuracy assessment using independent checkpoints is recommended to evaluate the geometric transformation between source and destination coordinate systems.

Camera The camera model is derived by space resection based on collinearity equations and is used for rectifying any image that uses a camera as its sensor.

The Camera Properties Dialog Box

In addition to the General, Links, and Elevation tabs, the Camera Properties dialog has tabs for Orientation, Camera, and Fiducials.

x0 ak xi j– yÞj

Þ

j 0=

i

∑i 0=

5

∑=

y0 bk xi j– yjÞ Þ

j 0=

i

∑i 0=

5

∑=


Orientation Tab The Orientation tab lets you choose rotation angles and center positions for the camera.

Figure 47: Orientation Tab

The rotation angle lets you customize the Omega, Phi, and Kappa rotation angles of the image to determine the viewing direction of the camera. You can choose from the following options:

• Unknown – Rotation angle is unknown.

• Estimated – Rotation angle is estimated.

• Fixed – Rotation angle is defined.

• Omega – Rotation angle is roll (around the x-axis of the ground system).

• Phi – Phi rotation angle is pitch (around the y-axis (after Omega rotation)).

• Kappa – Kappa rotation angle is yaw (around the z-axis rotated by Omega and Phi).

The perspective center position is given in meters and lets you enter the perspective center for ground coordinates. You can choose from the following options:

• Unknown – Select when the ground coordinate is unknown.

• Estimated – Select when estimating the ground coordinate.


• Fixed – Select when ground coordinate is defined.

• X – Enter the X coordinate of the perspective center.

• Y – Enter the Y coordinate of the perspective center.

• Z – Enter the Z coordinate of the perspective center.

Note: If you fill in all the degrees and meters for the rotation angle and the perspective center position, you do not need the three links normally required for the Camera model. If you fill in this information, do not check the Account for Earth’s curvature check box on the Elevation tab.

Camera Tab The next to last tab on the Camera Properties dialog is also called Camera. This is where you specify the camera name, the number of fiducials, the principal point, and the focal length for the camera that was used to capture your image.

Figure 48: Camera Tab

You can click Load or Save to open or save a file with camera information in it.

Fiducials Tab The last tab on the Camera Properties dialog is the Fiducials tab. Fiducials are used to compute the transformation from data file to image coordinates.

Figure 49: Fiducials Tab


Fiducial orientation defines the relationship between the image/photo-coordinate system of a frame and the actual image orientation as it displays in a view. The image/photo-coordinate system is defined by the camera calibration information. The orientation of the image is largely dependent on the way the photograph is scanned during the digitization stage.The fiducials for your image are fixed on the frame and are visible in the exposure. The fiducial information you enter on the Camera tab displays in a CellArray on the Fiducials tab after you click the Apply button in the Camera Properties dialog.Compare the axis of the photo-coordinate system (defined in the calibration report) with the orientation of the image to select the appropriate fiducial orientation. Based on the relationship between the photo-coordinate system and the image, you can select the appropriate fiducial orientation. Do not use more than eight fiducials in an image.


The fiducial orientations are used under the following circumstances:

Fiducial One – The marker is to the left of the image.

Fiducial Two – The marker is at the top of the image.

Fiducial Three – The marker is to the right of the image.

Fiducial Four – The marker is at the bottom of the image.

Fiducial Measurement – The software zooms in on the first fiducial you need to collect.Note: Selecting an inappropriate fiducial orientation results in large RMSEs during the measurement of fiducial marks for interior orientation and errors during the automatic tie point collection. Ensure that the appropriate fiducial orientation is used as a function of the image/photo-coordinate system.


IKONOS, QuickBird, and RPC Properties

IKONOS, QuickBird, and Rational Polynomial Coefficients (RPC) properties are sometimes referred to together as the Rational Function models. They are virtually the same except for the files they use, and their dialogs in GeoCorrection properties are identical as well. The differences are:

• IKONOS files are images captured by the IKONOS satellite.

• QuickBird files are images captured by the QuickBird satellite.

• RPC Properties uses National Imagery Transmission Format Standard (NITF) data.

Note: It is very important that you click the Add Links button before clicking the GeoCorrection Properties button to open one of these properties dialogs.

IKONOS IKONOS images are produced by the IKONOS satellite, which was launched in September of 1999 by the Athena II rocket. The resolution of the panchromatic sensor is 1 m. The resolution of the multispectral scanner is 4 m. The swath width is 13 km at nadir. The accuracy without ground control is 12 m horizontally, and 10 m vertically; with ground control it is 2 m horizontally, and 3 m vertically. IKONOS orbits at an altitude of 423 miles, or 681 kilometers. The revisit time is 2.9 days at 1 m resolution, and 1.5 days at 1.5 m resolution.The multispectral bands are as follows:

Table 9: IKONOS Bands and Wavelengths

The IKONOS Properties dialog lets you rectify IKONOS images from the satellite. Like the other properties dialogs in GeoCorrection Properties, IKONOS has General, Links, and Elevation tabs as well as Parameters and Chipping tabs.

Band Wavelength (Microns)

1, Blue 0.45 to 0.52 µm

2, Green 0.52 to 0.60 µm

3, Red 0.63 to 0.69 µm

4, NIR 0.76 to 0.90 µm

Panchromatic 0.45 to 0.90 µm


The RPC file is generated by the data provider based on the position of the satellite at the time of image capture. You can further refine the RPCs by using GCPs. Locate this file in the same directory as the image you intend to use in the GeoCorrection process.

QuickBird QuickBird properties let you rectify images captured by the QuickBird satellite. Like IKONOS, QuickBird requires the use of an RPC file to describe the relationship between the image and the Earth’s surface at the time of image capture.The QuickBird satellite was launched in October 2001. Its orbit has an altitude of 450 kilometers, a 93.5 minute orbit time, and a 10:30 A.M. equator crossing time. The inclination is 97.2 degrees sun-synchronous, and the nominal swath width is 16.5 kilometers at nadir. The sensor has both panchromatic and multispectral capabilities. The dynamic range is 11 bits per pixel for both panchromatic and multispectral. The panchromatic bandwidth is 450-900 nanometers.The multispectral bands are as follows:

Table 10: QuickBird Bands and Wavelengths

Just like IKONOS, QuickBird has a Parameters tab as well as a Chipping tab on its properties dialog. The same information applies to both tabs as is discussed in IKONOS, QuickBird, and RPC Properties on page 181.

RPC RPC properties let you specify the associated RPC file to use in geocorrection. RPC properties in Image Analysis for ArcGIS let you work with NITF data.NITF data is designed to pack numerous image compositions with complete annotation, text attachments, and imagery-associated metadata.The RPC file associated with the image contains rational function polynomial coefficients generated by the data provider based on the position of the satellite at the time of image capture. You can further refine these RPCs by using GCPs. Locate this file in the same directory as the images you intend to use in orthorectification.

Band Wavelength (Microns)

1, Blue 0.45 to 0.52 µm

2, Green 0.52 to 0.60 µm

3, Red 0.63 to 0.69 µm

4, NIR 0.76 to 0.90 µm


Just like IKONOS and QuickBird, the RPC Properties dialog contains the Parameters and Chipping tabs. These work the same way in all three model properties.

IKONOS, QuickBird, and RPC Parameters Tab

The Parameters tab on the IKONOS, QuickBird, and RPC Properties dialogs calls for an RPC file and the elevation range. Be sure to enter the RPC file information first.

Figure 50: IKONOS, QuickBird, and RPC – Parameters Tab

There is also a check box for Refinement with Polynomial Order. This is provided so you can apply polynomial corrections to the original rational function model. This setting corrects the remaining error and refines the mathematical solution. Check the Refinement with Polynomial Order check box to enable the refinement process, and then specify the order by clicking the arrows.The 0 order results in a simple shift to both image X and Y coordinates. The 1st order is an affine transformation. The 2nd order results in a 2nd order transformation, and the 3rd order in a 3rd order transformation. Usually, a 0 or 1st order is sufficient to reduce error not addressed by the rational function model (RPC file).The fields in the Elevation Range box are automatically populated by the RPC file.

IKONOS, QuickBird, and RPC Chipping Tab

The chipping process allows circulation of RPCs for an image chip rather than the full, original image from which the chip was derived. This is made possible by specifying an affine relationship (pixel) between the chip and the full, original image. The Chipping tab is the same for IKONOS, QuickBird, and RPC Properties.


Figure 51: Chipping Tab

You are given the choice of Scale and Offset or Arbitrary Affine as your chipping parameters on the Chipping tab. The tab changes depending on which chipping parameter you select from the Specify Chipping Parameters As dropdown list as described in IKONOS, QuickBird, and RPC Properties on page 185 and IKONOS, QuickBird, and RPC Properties on page 186.Full Row Count and Full Column Count fields are located at the bottom of the Chipping tab. If the chip header contains the appropriate data, the Full Row Count value is the row count of the full, original image. If the header count is absent, this value corresponds to the row count of the chip.


Scale and Offset Scale and Offset is the simpler of the two chipping parameters. The formulas for calculating the affine using scale and offset are listed in a box on the Chipping tab.

Figure 52: Chipping Tab using Scale and Offset

X and Y correspond to the pixel coordinates for the full, original image. The options are:

• Row Offset – This value corresponds to value f, an offset value. In the absence of header data, this value defaults to 0.

• Row Scale – This value corresponds to value e, a scale factor that is also used in rotation. In the absence of header data, this value defaults to 1.

• Column Offset – This value corresponds to value c, an offset value. In the absence of header data, this value defaults to 0.

• Column Scale – This value corresponds to value a, a scale factor that is also used in rotation. In the absence of header data, this value defaults to 1.


Arbitrary Affine The Arbitrary Affine formulas display in the box on the Chipping tab when you select that option from the Specify chipping parameters as dropdown list. In the formulas, x’ (x prime), and y’ (y prime), correspond to the pixel coordinates in the chip with which you are currently working. Values for the variables are either obtained from the header data of the chip, or they default to the predetermined values described above.The following is an example of the Arbitrary Affine settings on the Chipping tab.

Figure 53: Chipping Tab using Arbitrary Affine

Landsat The Landsat dialog is used for orthorectification of any Landsat image that uses TM or MSS as its sensor. The model is derived by space resection based on collinearity equations. The elevation information is required in the model for removing relief displacement.

Landsat 1-5 In 1972, the National Aeronautics and Space Administration (NASA) initiated the first civilian program specializing in the acquisition of remotely sensed digital satellite data. The first system was called Earth Resources Technology Satellites (ERTS), and later renamed Landsat. There have been several Landsat satellites launched since 1972. Landsats 1, 2, and 3 are no longer operating, but Landsats 4 and 5 are still in orbit gathering data. Landsats 1, 2, and 3 gathered Multispectral Scanner (MSS) data and Landsats 4 and 5 collect MSS and TM data.


MSS The MSS from Landsats 4 and 5 has a swath width of approximately 185 × 170 km from a height of approximately 900 km for Landsats 1, 2, and 3, and 705 km for Landsats 4 and 5. MSS data is widely used for general geologic studies as well as vegetation inventories.The spatial resolution of MSS data is 56 × 79 m, with a 79 × 79 m IFOV (instantaneous field of view). A typical scene contains approximately 2340 rows and 3240 columns. The radiometric resolution is 6-bit, but it is stored as 8-bit (Lillesand and Kiefer 1987).Detectors record electromagnetic radiation (EMR) in four bands:

• Bands 1 and 2 – Are in the visible portion of the spectrum and are useful in detecting cultural features, such as roads. These bands also show detail in water.

• Bands 3 and 4 – Are in the near-infrared portion of the spectrum and are useful in land/water and vegetation discrimination.

• Bands 4, 3, and 2 – Create a false color composite. False color composites appear similar to an infrared photograph where objects do not have the same colors or contrasts as they would naturally. For example, in an infrared image, vegetation appears red, water appears navy or black, and so on.

• Bands 5, 4, and 2 – Create a pseudo color composite. (A thematic image is also a pseudo color image.) In pseudo color, the colors do not reflect the features in natural colors. For example, roads might be red, water yellow, and vegetation blue.

You can use different color schemes to enhance the features under study. These are by no means all of the useful combinations of the seven bands. The particular applications determine the bands to use.

TM The TM scanner is a multispectral scanning system much like MSS, except the TM sensor records reflected/emitted electromagnetic energy from the visible, reflective-infrared, middle-infrared, and thermal-infrared regions of the spectrum. TM has higher spatial, spectral, and radiometric resolution than MSS.TM has a swath width of approximately 185 km from a height of approximately 705 km. It is useful for vegetation type and health determination, soil moisture, snow and cloud differentiation, rock type discrimination, and so on. The spatial resolution of TM is 28.5 × 28.5 m for all bands except the thermal (band 6), which has a spatial resolution of 120 × 120 m. The larger pixel size of this band is necessary for adequate signal strength. However, the thermal band is resampled to 28.5 × 28.5 m to match the other bands. The radiometric resolution is 8-bit, meaning that each pixel has a possible range of data values from 0 to 255.Detectors record EMR in seven bands:


• Bands 1, 2, and 3 – Are in the visible portion of the spectrum and are used in detecting cultural features such as roads. These bands also show detail in water.

• Bands 4, 5, and 7 – Are in the reflective-infrared portion of the spectrum and are used in land/water discrimination.

• Band 6 – Is in the thermal portion of the spectrum and is used for thermal mapping (Jensen 1996; Lillesand and Kiefer 1987).

Table 11: TM Bands and Wavelengths

Band Wave-length (Microns)

Comments

1, Blue 0.45 to 0.52 µm

Differentiates between soil and vegetation, forest type mapping, and detecting cultural features for mapping coastal water areas.

2, Green 0.52 to 0.60 µm

Corresponds to the green reflectance of healthy vegetation. Also useful for cultural feature identification.

3, Red 0.63 to 0.69 µm

Differentiates between many plant species. It is also useful for determining soil boundary and geological boundary delineations as well as cultural features.

4, NIR 0.76 to 0.90 µm

Indicates the amount of vegetation biomass present in a scene. It is useful for crop identification and emphasizes soil/crop and land/water contrasts.

5, MIR 1.55 to 1.75 µm

Detects the amount of water in plants, which is useful in crop drought studies and in plant health analyses. This is also one of the few bands that is used to discriminate between clouds, snow, and ice.

6, TIR 10.40 to 12.50 µm

Detects vegetation and crop stress, heat intensity, insecticide applications, and thermal pollution. It is also used to locate geothermal activity.

7, MIR 2.08 to 2.35 µm

Discriminates between geologic rock type and soil boundaries, as well as soil and vegetation moisture content.


Figure 54: Landsat MSS versus Landsat TM

Band Combinations for Displaying TM Data

You can display different combinations of TM bands to create different composite effects. The order of the bands corresponds to the RGB color guns of the monitor. The following combinations are commonly used to display images:

• Bands 3, 2, 1 – Create a true color composite. True color means that objects look as they would to the naked eye—similar to a color photograph.

• Bands 4, 3, 2 – Create a false color composite. False color composites appear similar to an infrared photograph where objects do not have the same colors or contrasts as they would naturally. For example, in an infrared image, vegetation appears red, water appears navy or black, and so on.

• Bands 5, 4, 2 – Create a pseudo color composite. (A thematic image is also a pseudo color image.) In pseudo color, the colors do not reflect the features in natural colors. For example, roads may be red, water yellow, and vegetation blue.

You can use different color schemes to bring out or enhance the features under study. These are by no means all of the useful combinations of the seven bands. The application determines the bands to use.

Landsat 7 The Landsat 7 satellite, launched in 1999, uses Enhanced Thematic Mapper Plus (ETM+) to observe the Earth. The capabilities new to Landsat 7 include the following:

• 15 m spatial resolution panchromatic band

• 5% radiometric calibration with full aperture

RadiometricResolution0-127

RadiometricResolution0- 255



3 Bands

7 Bands

MSS

TM


• 60 m spatial resolution thermal IR channel

The primary receiving station for Landsat 7 data is located in Sioux Falls, South Dakota at the USGS EROS Data Center (EDC). ETM+ data is transmitted using X-band direct downlink at a rate of 150 Mbps. Landsat 7 is capable of capturing scenes without cloud obstruction, and the receiving stations can obtain this data in real time using the X-band. Stations located around the globe, however, only receive data for the portion of the ETM+ ground track where the receiving station can see the satellite.

Landsat 7 Data Types One type of data available from Landsat 7 is browse data. Browse data is a lower resolution image for determining image location, quality and information content. Another type of data is metadata, which is descriptive information on the image. This information is available via the internet within 24 hours of being received by the primary ground station. Moreover, EDC processes the data to Level 0r. This data is corrected for scan direction and band alignment errors only. Level 1G data, which is corrected, is also available.

Landsat 7 Specifications Information about the spectral range and ground resolution of the bands of the Landsat 7 satellite is provided in the following table:

Table 12: Landsat 7 Characteristics

Landsat 7 has a swath width of 185 kilometers. The repeat coverage interval is 16 days, or 233 orbits. The satellite orbits the Earth at 705 kilometers.

The Landsat Properties Dialog Box

The Landsat Properties dialog in GeoCorrection Properties has the General, Links, and Elevation tabs already discussed in this chapter. It also has a Parameters tab as discussed in the next section.

BandNumber

Wavelength(Microns)

Resolution(m)

1 0.45 to 0.52 µm 30

2 0.52 to 0.60 µm 30

3 0.63 to 0.69 µm 30

4 0.76 to 0.90 µm 30

5 1.55 to 1.75 µm 30

6 10.4 to 12.5 µm 60

7 2.08 to 2.35 µm 30

Panchromatic (8) 0.50 to 0.90 µm 15


Parameters Tab The Landsat Properties Parameters tab groups the settings for specifying the type of sensor used to capture your data, the Scene Coverage (if you choose Quarter Scene you also choose the quadrant), the Number of Iterations, and the Background.The Parameters tab is shown below.

Figure 55: Landsat Properties – Parameters Tab

193Glossary

Glossary 193

Glossaryabstract symbol

An annotation symbol that has a geometric shape, such as a circle, square, or triangle. These symbols often represent amounts that vary from place to place, such as population density, yearly rainfall, and so on.

accuracy assessmentThe comparison of a classification to geographical data that is assumed to be true. Usually, the assumed true data is derived from ground truthing.

American Standard Code for Information InterchangeA basis of character sets...to convey some control codes, space, numbers, most basic punctuation, and unaccented letters a-z and A-Z.

analysis maskAn option that uses a raster dataset in which all cells of interest have a value and all other cells have no data. Analysis mask lets you perform analysis on a selected set of cells.

ancillary dataThe data, other than remotely sensed data, that is used to aid in the classification process.

annotationThe explanatory material accompanying an image or a map. Annotation can consist of lines, text, polygons, ellipses, rectangles, legends, scale bars, and any symbol that denotes geographical features.

AOISee area of interest.

a prioriAlready or previously known.

areaA measurement of a surface.

area of interestA point, line, or polygon that is selected as a training sample or as the image area to use in an operation.

194 Glossary

ASCIISee American Standard Code for Information Interchange.

aspectThe orientation, or the direction that a surface faces, with respect to the directions of the compass: north, south, east, west.

attributeThe tabular information associated with a raster or vector layer.

averageThe statistical mean; the sum of a set of values divided by the number of values in the set.

bandA set of data file values for a specific portion of the electromagnetic spectrum of reflected light or emitted heat (red, green, blue, near-infrared, infrared, thermal, and so on) or some other user-defined information created by combining or enhancing the original bands, or creating new bands from other sources. Sometimes called channel.

bilinear interpolationUses the data file values of four pixels in a 2 × 2 window to calculate an output value with a bilinear function.

bin functionA mathematical function that establishes the relationship between data file values and rows in a descriptor table.

binsOrdered sets of pixels. Pixels are sorted into a specified number of bins. The pixels are then given new values based upon the bins to which they are assigned.

borderOn a map, a line that usually encloses the entire map, not just the image area as does a neatline.

boundaryA neighborhood analysis technique that is used to detect boundaries between thematic classes.

brightness valueThe quantity of a primary color (red, green, blue) for a pixel on the display device. Also called intensity value, function memory value, pixel value, display value, and screen value.

Glossary 195

buffer zoneA specific area around a feature that is isolated for or from further analysis. For example, buffer zones are often generated around streams in site assessment studies so that further analyses exclude these areas that are often unsuitable for development.

CartesianA coordinate system in which data is organized on a grid and points on the grid are referenced by their X,Y coordinates.

camera propertiesCamera properties are for the orthorectification of any image that uses a camera for its sensor. The model is derived by space resection based on collinearity equations. The elevation information is required in the model for removing relief displacement.

categorizeThe process of choosing distinct classes to divide your image into.

cell1. A 1 × 1 area of coverage. Digital terrain elevation data (DTED) is distributed in cells. 2. A pixel; grid cell.

cell sizeThe area that one pixel represents, measured in map units. For example, one cell in the image may represent an area 30’ × 30’ on the ground. Sometimes called the pixel size.

checkpoint analysisThe act of using checkpoints to independently verify the degree of accuracy of a triangulation.

circumcircleA triangle’s circumscribed circle; the circle that passes through each of the triangle’s three vertices.

classA set of pixels in a GIS file that represents areas that share some condition. Classes are usually formed through classification of a continuous raster layer.

class valueA data file value of a thematic file that identifies a pixel as belonging to a particular class.

196 Glossary

classificationThe process of assigning the pixels of a continuous raster image to discrete categories.

classification accuracy tableFor accuracy assessment, a list of known values of reference pixels, supported by some ground truth or other a priori knowledge of the true class, and a list of the classified values of the same pixels, from a classified file to be tested.

classification scheme (or classification system)A set of target classes. The purpose of such a scheme is to provide a framework for organizing and categorizing the information that is extracted from the data.

clusteringUnsupervised training; the process of generating signatures based on the natural groupings of pixels in image data when they are plotted in spectral space.

clustersThe natural groupings of pixels when plotted in spectral space.

coefficientOne number in a matrix, or a constant in a polynomial expression.

collinearityA nonlinear mathematical model that photogrammetric triangulation is based upon. Collinearity equations describe the relationship among image coordinates, ground coordinates, and orientation parameters.

contiguity analysisA study of the ways in which pixels of a class are grouped together spatially. Groups of contiguous pixels in the same class, called raster regions, or clumps, can be identified by their sizes and multiplied.

continuousA term used to describe raster data layers that contain quantitative and related values. See also continuous data.

continuous dataA type of raster data that is quantitative (measuring a characteristic) and has related, continuous values, such as remotely sensed images (Landsat, SPOT, and so on).

Glossary 197

contrast stretchThe process of reassigning a range of values to another range, usually according to a linear function. Contrast stretching is often used in displaying continuous raster layers because the range of data file values is usually much narrower than the range of brightness values on the display device.

convolution filteringThe process of averaging small sets of pixels across an image. Used to change the spatial frequency characteristics of an image.

convolution kernelA matrix of numbers that is used to average the value of each pixel with the values of surrounding pixels in a particular way. The numbers in the matrix serve to weight this average towards particular pixels.

coordinate systemA method of expressing location. In two-dimensional coordinate systems, locations are expressed by a column and row, also called X and Y.

correlation thresholdA value used in rectification to determine whether to accept or discard GCPs. The threshold is an absolute value threshold ranging from 0.000 to 1.000.

correlation windowsWindows that consist of a local neighborhood of pixels.

corresponding GCPsThe GCPs that are located in the same geographic location as the selected GCPs, but are selected in different files.

covarianceMeasures the tendencies of data file values for the same pixel, but in different bands, to vary with each other in relation to the means of their respective bands. These bands must be linear. Covariance is defined as the average product of the differences between the data file values in each band and the mean of each band.

covariance matrixA square matrix that contains all of the variances and covariances within the bands in a data file.

198 Glossary

cubic convolutionUses the data file values of sixteen pixels in a 4 × 4 window to calculate an output with cubic function.

data1. In the context of remote sensing, a computer file containing numbers that represent a remotely sensed image, and can be processed to display that image. 2. A collection of numbers, strings, or facts that requires some processing before it is meaningful.

databaseA relational data structure usually used to store tabular information. Examples of popular databases include SYBASE, dBASE, Oracle, and INFO.

data fileA computer file that contains numbers that represent an image.

data file valueEach number in an image file. Also called file value, image file value, DN, brightness value, pixel.

decision ruleAn equation or algorithm that is used to classify image data after signatures are created. The decision rule is used to process the data file values based on the signature statistics.

DEMSee digital elevation model.

densityA neighborhood analysis technique that displays the number of pixels that have the same value as the analyzed pixel in a user-specified window.

digital elevation modelContinuous raster layers in which data file values represent elevation. DEMs are available from the USGS at 1:24,000 and 1:250,000 scale, and can be produced with terrain analysis programs.

digital terrain modelA discrete expression of topography in a data array, consisting of a group of planimetric coordinates (X,Y) and the elevations of the ground points and breaklines.

Glossary 199

dimensionalityIn classification, dimensionality refers to the number of layers being classified. For example, a data file with three layers is said to be three-dimensional.

divergenceA statistical measure of distance between two or more signatures. Divergence can be calculated for any combination of bands used in the classification; bands that diminish the results of the classification can be ruled out.

diversityA neighborhood analysis technique that displays the number of different values in a user-specified window.

DTMSee digital terrain model.

edge detectorA convolution kernel, which is usually a zero sum kernel, that smooths out or zeros out areas of low spatial frequency and creates a sharp contrast where spatial frequency is high. High spatial frequency is at the edges between homogeneous groups of pixels.

edge enhancerA high-frequency convolution kernel that brings out the edges between homogeneous groups of pixels. Unlike an edge detector, it only highlights edges; it does not eliminate other features.

enhancementThe process of making an image more interpretable for a particular application. Enhancement can make important features of raw, remotely sensed data more interpretable to the human eye.

extensionThe three letters after the period in a file name that usually identify the type of file.

extent1. The image area to display in a view. 2. The area of the Earth’s surface to map.

200 Glossary

feature collectionThe process of identifying, delineating, and labeling various types of natural and human-made phenomena from remotely sensed images.

feature extractionThe process of studying and locating areas and objects on the ground and deriving useful information from images.

feature spaceAn abstract space that is defined by spectral units (such as an amount of electromagnetic radiation).

fiducial centerThe center of an aerial photo.

fiducialsFour or eight reference markers fixed on the frame of an aerial metric camera and visible in each exposure that are used to compute the transformation from data file to image coordinates.

file coordinatesThe location of a pixel within the file in X,Y coordinates. The upper-left file coordinate is usually 0,0.

filteringThe removal of spatial or spectral features for data enhancement. Convolution filtering is one method of spatial filtering. Some texts use the terms filtering and spatial filtering synonymously.

focalThe process of performing one of several analyses on data values in an image file, using a process similar to convolution filtering.

GCPSee ground control point.

GCP matchingFor image-to-image rectification, a GCP selected in one image is precisely matched to its counterpart in the other image using the spectral characteristics of the data and the transformation matrix.

geocorrectionThe process of rectifying remotely sensed data that has distortions due to a sensor or the curvature of the Earth.

Glossary 201

geographic information systemA unique system designed for a particular application that stores, enhances, combines, and analyzes layers of geographic data to produce interpretable information. A GIS might include computer images, hardcopy maps, statistical data, and any other data needed for a study, as well as computer software and human knowledge. GISs are used for solving complex geographic planning and management problems.

georeferencingThe process of assigning map coordinates to image data and resampling the pixels of the image to conform to the map projection grid.

GISSee geographic information system.

ground control pointSpecific pixel in image data for which the output map coordinates (or other output coordinates) are known. GCPs are used for computing a transformation matrix, for use in rectifying an image.

high-frequency kernelA convolution kernel that increases the spatial frequency of an image. Also called a high-pass kernel.

histogramA graph of data distribution, or a chart of the number of pixels that have each possible data file value. For a single band of data, the horizontal axis of a histogram graph is the range of all possible data file values. The vertical axis is a measure of pixels that have each data value.

histogram equalizationThe process of redistributing pixel values so that there are approximately the same number of pixels with each value within a range. The result is a nearly flat histogram.

histogram matchingThe process of determining a lookup table that converts the histogram of one band of an image or one color gun to resemble another histogram.

hueA component of intensity, hue, saturation that is representative of the color or dominant wavelength of the pixel. It varies from 0

202 Glossary

to 360. Blue = 0 (and 360), magenta = 60, red = 120, yellow = 180, green = 240, and cyan = 300.

IKONOS propertiesUse the IKONOS Properties dialog to perform orthorectification on images gathered with the IKONOS satellite. The IKONOS satellite orbits at an altitude of 423 miles, or 681 kilometers. The revisit time is 2.9 days at 1 m resolution, and 1.5 days at 1.5 m resolution.

image dataDigital representations of the Earth that can be used in computer image processing and GIS analyses.

image fileA file containing raster image data.

image matchingThe automatic acquisition of corresponding image points on the overlapping area of two images.

image processingThe manipulation of digital image data, including (but not limited to) enhancement, classification, and rectification operations.

indicesThe process used to create output images by mathematically combining the DN values of different bands.

infraredInfrared portion of the electromagnetic spectrum.

IRSee infrared.

island polygonsWhen using the Seed tool, island polygons represent areas in the polygon that have different characteristics from the areas in the larger polygon. You have the option of using the island polygons feature or turning it off when using the Seed tool.

ISODATASee Iterative Self-Organizing Data Analysis Technique.

Iterative Self-Organizing Data Analysis TechniqueA method of clustering that uses spectral distance as in the sequential method, but iteratively classifies the pixels,

Glossary 203

redefines the criteria for each class, and classifies again so that the spectral distance patterns in the data gradually emerge.

LandsatA series of Earth-orbiting satellites that gather MSS and TM imagery operated by EOSAT.

layer1. A band or channel of data. 2. A single band or set of three bands displayed using the red, green, and blue color guns. 3. A component of a GIS database that contains all of the data for one theme. A layer consists of a thematic image file, and may also include attributes.

linearA description of a function that can be graphed as a straight line or a series of lines. Linear equations (transformations) are generally expressed in the form of the equation of a line or plane. Also called 1st order.

linear contrast stretchAn enhancement technique that produces new values at regular intervals.

linear transformationA 1st order rectification. A linear transformation can change location in X or Y, scale in X or Y, skew in X or Y, and rotation.

lookup tableAn ordered set of numbers that is used to perform a function on a set of input values. Lookup tables translate data file values into brightness values to display or print an image.

low-frequency kernelA convolution kernel that decreases spatial frequency. Also called low-pass kernel.

LUTSee lookup table.

majorityA neighborhood analysis technique that displays the most common value of the data file values in a user-specified window.

map projectionA method of representing the three-dimensional spherical surface of a planet on a two-dimensional map surface. All map

204 Glossary

projections involve the transfer of latitude and longitude onto an easily flattened surface.

maximumA neighborhood analysis technique that displays the greatest value of the data file values in a user-specified window.

maximum likelihoodA classification decision rule based on the probability that a pixel belongs to a particular class. The basic equation assumes that these probabilities are equal for all classes, and that the input bands have normal distributions.

mean1. The statistical average; the sum of a set of values divided by the number of values in the set. 2. A neighborhood analysis technique that displays the mean value of the data file values in a user-specified window.

median1. The central value in a set of data such that an equal number of values are greater than and less than the median. 2. A neighborhood analysis technique that displays the median value of the data file values in a user-specified window.

minimumA neighborhood analysis technique that displays the least value of the data file values in a user-specified window.

minimum distanceA classification decision rule that calculates the spectral distance between the measurement vector for each candidate pixel and the mean vector for each signature. Also called spectral distance.

minorityA neighborhood analysis technique that displays the least common value of the data file values in a user-specified window.

modelingThe process of creating new layers from combining or operating upon existing layers. Modeling allows the creation of new classes from existing classes and the creation of a small set of images or a single image, which at a glance, contains many types of information about a scene.

Glossary 205

mosaickingThe process of piecing together images side-by-side to create a larger image.

MSSSee multispectral scanner.

multispectral classificationThe process of sorting pixels into a finite number of individual classes, or categories of data, based on data file values in multiple bands.

multispectral imagerySatellite imagery with data recorded in two or more bands.

multispectral scannerLandsat satellite data acquired in four bands with a spatial resolution of 57 × 79 meters.

nadirThe area on the ground directly beneath a scanner’s detectors.

NDVISee normalized difference vegetation index.

nearest neighborA resampling method in which the output data file value is equal to the input pixel that has coordinates closest to the retransformed coordinates of the output pixel.

neighborhood analysisAny image processing technique that takes surrounding pixels into consideration, such as convolution filtering and scanning.

NoDataNoData is what you assign to pixel values you do not want to include in a classification or function. By assigning pixel values NoData, they are not given a value. Images that georeference to non-rectangles need a NoData concept for display even if they are not classified. The values that NoData pixels are given are understood to be just place holders.

non-directionalThe process using the Sobel and Prewitt filters for edge detection. These filters use orthogonal kernels convolved separately with the original image and then combined.

206 Glossary

nonlinearDescribing a function that cannot be expressed as the graph of a line or in the form of the equation of a line or plane. Nonlinear equations usually contain expressions with exponents. Second order (2nd order) or higher-order equations and transformations are nonlinear.

nonlinear transformationA 2nd order or higher rectification.

nonparametric signatureA signature for classification that is based on polygons or rectangles that are defined in the feature space image for the image file. There is no statistical basis for a nonparametric signature; it is an area in a feature space image.

normalized difference vegetation indexThe formula for NDVI is IR - R / IR + R, where IR stands for the infrared portion of the electromagnetic spectrum, and R stands for the red portion of the electromagnetic spectrum. NDVI finds areas of vegetation in imagery.

observationIn photogrammetric triangulation, a grouping of the image coordinates for a GCP.

off-nadirAny point that is not directly beneath a scanner’s detectors, but off to an angle. The SPOT scanner allows off-nadir viewing.

orthorectificationA form of rectification that corrects for terrain displacement and is used if a DEM of the study area is available.

overlay1. A function that creates a composite file containing either the minimum or the maximum class values of the input files. Overlay sometimes refers generically to a combination of layers. 2. The process of displaying a classified file over the original image to inspect the classification.

panchromatic imagerySingle-band or monochrome satellite imagery.

parallelepiped1. A classification decision rule in which the data file values of the candidate pixel are compared to upper and lower limits. 2.

Glossary 207

The limits of a parallelepiped classification, especially when graphed as rectangles.

parameter1. Any variable that determines the outcome of a function or operation. 2. The mean and standard deviation of data, which are sufficient to describe a normal curve.

parametric signatureA signature that is based on statistical parameters (such as mean and covariance matrix) of the pixels that are in the training sample or cluster.

pattern recognitionThe science and art of finding meaningful patterns in data, which can be extracted through classification.

PCASee principal components analysis.

piecewise linear contrast stretchAn enhancement technique used to enhance a specific portion of data by dividing the lookup table into three sections: low, middle, and high.

pixelAbbreviated from picture element; the smallest part of a picture (image).

pixel depthThe number of bits required to store all of the data file values in a file. For example, data with a pixel depth of 8, or 8-bit data, have 256 values ranging from 0-255.

pixel sizeThe physical dimension of a single light-sensitive element (13 × 13 microns).

polygonA set of closed line segments defining an area.

polynomialA mathematical expression consisting of variables and coefficients. A coefficient is a constant that is multiplied by a variable in the expression.

208 Glossary

principal components analysis1. A method of data compression that allows redundant data to be compressed into fewer bands (Jensen 1996; Faust 1989). 2. The process of calculating principal components and producing principal component bands. It allows redundant data to be compacted into fewer bands (that is the dimensionality of the data is reduced).

principal pointThe point in the image plane onto which the perspective center is projected, located directly beneath the interior orientation.

profileA row of data file values from a DEM or DTED file. The profiles of DEM and DTED run south to north (the first pixel of the record is the southernmost pixel).

pushbroomA scanner in which all scanning parts are fixed, and scanning is accomplished by the forward motion of the scanner, such as the SPOT scanner.

QuickBirdThe QuickBird model requires the use of rational polynomial coefficients (RPCs) to describe the relationship between the image and the Earth's surface at the time of image capture. By using QuickBird properties, you can perform orthorectification on images gathered with the QuickBird satellite.

radar dataThe remotely sensed data that is produced when a radar transmitter emits a beam of micro or millimeter waves. The waves reflect from the surfaces they strike, and the backscattered radiation is detected by the radar system’s receiving antenna, which is tuned to the frequency of the transmitted waves.

radiometric correctionThe correction of variations in data that is not caused by the object or scene being scanned, such as scanner malfunction and atmospheric interference.

radiometric enhancementAn enhancement technique that deals with the individual values of pixels in an image.

Glossary 209

radiometric resolutionThe dynamic range, or number of possible data file values, in each band. This is referred to by the number of bits into which the recorded energy is divided. See also pixel depth.

rankA neighborhood analysis technique that displays the number of values in a user-specified window that are less than the analyzed value.

raster dataA data type in which thematic class values have the same properties as interval values, except that ratio values have a natural zero or starting point.

rational polynomial coefficientsSee RPC properties.

recodingThe assignment of new values to one or more classes.

rectificationThe process of making image data conform to a map projection system. In many cases, the image must also be oriented so that the north direction corresponds to the top of the image.

rectified coordinatesThe coordinates of a pixel in a file that have been rectified, which are extrapolated from the GCPs. Ideally, the rectified coordinates for the GCPs are exactly equal to the reference coordinates. Because there is often some error tolerated in the rectification, this is not always the case.

red, green, blueThe primary additive colors that are used on most display hardware to display imagery.

reference coordinatesThe coordinates of the map or reference image to which a source (input) image is being registered. GCPs consist of both input coordinates and reference coordinates for each point.

reference pixelsIn classification accuracy assessment, pixels for which the correct GIS class is known from ground truth or other data. The reference pixels can be selected by you, or randomly selected.

210 Glossary

reference planeIn a topocentric coordinate system, the tangential plane at the center of the image on the Earth ellipsoid, on which the three perpendicular coordinate axes are defined.

reprojectTransforms raster image data from one map projection to another.

resamplingThe process of extrapolating data file values for the pixels in a new grid when data has been rectified or registered to another image.

resolutionA level of precision in data.

resolution mergeThe process of sharpening a lower-resolution multiband image by merging it with a higher-resolution monochrome image.

RGBSee red, green, blue.

RGB clusteringA clustering method for 24-bit data (three 8-bit bands) that plots pixels in three-dimensional spectral space and divides that space into sections that are used to define clusters. The output color scheme of an RGB-clustered image resembles that of the input file.

RMSESee root mean square error.

Root mean square errorThe distance between the input (source) location of the GCP and the retransformed location for the same GCP. RMS error is calculated with a distance equation.

RPCSee rational polynomial coefficients.

RPC propertiesThe RPC properties uses rational polynomial coefficients to describe the relationship between the image and the Earth's surface at the time of image capture. You can specify the associated RPC file to use in your geocorrection.

Glossary 211

Rubber SheetingThe application of nonlinear rectification (2nd order or higher).

saturationA component of IHS that represents the purity of color and also varies linearly from 0 to 1.

scale1. The ratio of distance on a map as related to the true distance on the ground. 2. Cell size. 3. The processing of values through a lookup table.

scannerThe entire data acquisition system such as the Landsat scanner or the SPOT panchromatic scanner.

seed toolAn Image Analysis for ArcGIS feature that automatically generates feature layer polygons of similar spectral value.

shapefileA vector format that contains spatial data. Shapefiles have the .shp extension.

signatureA set of statistics that defines a training sample or cluster. The signature is used in a classification process. Each signature corresponds to a GIS class that is created from the signatures with a classification decision rule.

source coordinatesIn the rectification process, the input coordinates.

spatial enhancementThe process of modifying the values of pixels in an image relative to the pixels that surround them.

spatial frequencyThe difference between the highest and lowest values of a contiguous set of pixels.

spatial resolutionA measure of the smallest object that can be resolved by the sensor, or the area on the ground represented by each pixel.

speckle noiseThe light and dark pixel noise that appears in radar data.

212 Glossary

spectral distanceThe distance in spectral space computed as Euclidean distance in n-dimensions, where n is the number bands.

spectral enhancementThe process of modifying the pixels of an image based on the original values of each pixel, independent of the values of surrounding pixels.

spectral resolutionA measure of the smallest object that can be resolved by the sensor, or the area on the ground represented by each pixel.

spectral spaceAn abstract space that is defined by spectral units (such as an amount of electromagnetic radiation). The notion of spectral space is used to describe enhancement and classification techniques that compute the spectral distance between n-dimensional vectors, where n is the number of bands in the data.

SPOTSPOT satellite sensors operate in two modes—multispectral and panchromatic. SPOT is often referred to as the pushbroom scanner, meaning that all scanning parts are fixed, and scanning is accomplished by the forward motion of the scanner. SPOT pushes 3000/6000 sensors along its orbit. This differs from Landsat, which scans with 16 detectors perpendicular to its orbit.

standard deviation1. The square root of the variance of a set of values used as a measurement of the spread of the values. 2. A neighborhood analysis technique that displays the standard deviation of the data file values of a user-specified window.

stripingA data error that occurs if a detector on a scanning system goes out of adjustment; that is, it provides readings consistently greater than or less than the other detectors for the same band over the same ground cover.

subsettingThe process of breaking out a portion of a large image file into one or more smaller files.

Glossary 213

sumA neighborhood analysis technique that displays the total of the data file values in a user-specified window.

supervised trainingAny method of generating signatures for classification in which the analyst is directly involved in the pattern recognition process. Usually, supervised training requires the analyst to select training samples from the data that represent patterns to be classified.

swath widthIn a satellite system, the total width of the area on the ground covered by the scanner.

summarize areasA common workflow progression with a feature theme corresponding to an area of interest to summarize the change just within a certain area.

temporal resolutionThe frequency with which a sensor obtains imagery of a particular area.

terrain analysisThe processing and graphic simulation of elevation data.

terrain dataElevation data expressed as a series of x, y, and z values that are either regularly or irregularly spaced.

thematic changeA feature in Image Analysis for ArcGIS that lets you compare two thematic images of the same area captured at different times to notice changes in vegetation, urban areas, and so on.

thematic dataRaster data that is qualitative and categorical. Thematic layers often contain classes of related information, such as land cover, soil type, slope, and so on.

thematic mapA map illustrating the class characterizations of a particular spatial variable such as soils, land cover, hydrology, and so on.

thematic mapperLandsat data acquired in seven bands with a spatial resolution of 30 × 30 meters.

214 Glossary

themeA particular type of information, such as soil type or land use, that is represented in a layer.

thresholdA limit, or cutoff point, usually a maximum allowable amount of error in an analysis. In classification, thresholding is the process of identifying a maximum distance between a pixel and the mean of the signature to which it was classified.

TMSee thematic mapper.

trainingThe process of defining the criteria by which patterns in image data are recognized for the purpose of classification.

training sampleA set of pixels selected to represent a potential class. Also called sample.

transformation matrixA set of coefficients that is computed from GCPs, and used in polynomial equations to convert coordinates from one system to another. The size of the matrix depends upon the order of the transformation.

triangulationEstablishes the geometry of the camera or sensor relative to objects on the Earth’s surface.

true colorA method of displaying an image (usually from a continuous raster layer) that retains the relationships between data file values and represents multiple bands with separate color guns. The image memory values from each displayed band are translated through the function memory of the corresponding color gun.

unsupervised trainingA computer-automated method of pattern recognition in which some parameters are specified by the user and are used to uncover statistical patterns that are inherent in the data.

variable1. A numeric value that is changeable, usually represented with a letter. 2. A thematic layer. 3. One band of a multiband image.

Glossary 215

4. In models, objects that have been associated with a name using a declaration statement.

vector dataData that represents physical forms (elements) such as points, lines, and polygons. Only the vertices of vector data are stored, instead of every point that makes up the element.

vegetative indicesA grayscale image that clearly highlights vegetation.

zoomThe process of expanding displayed pixels on an image so they can be more closely studied. Zooming is similar to magnification, except that it changes the display only temporarily, leaving image memory the same.

216 Glossary

217References

References 217

ReferencesThe following references were used in the creation of this book.

Akima, H., 1978, A Method for Bivariate Interpolation and Smooth Surface Fitting for Irregularly Distributed Data Points, ACM Transactions on Mathematical Software 4(2), pp. 148-159.

Buchanan, M.D. 1979. “Effective Utilization of Color in Multidimensional Data Presentation.” Proceedings of the Society of Photo-Optical Engineers, Vol. 199: 9-19.

Chavez, Pat S., Jr, et al. 1991. “Comparison of Three Different Methods to Merge Multiresolution and Multispectral Data: Landsat TM and SPOT Panchromatic.” Photogrammetric Engineering & Remote Sensing, Vol. 57, No. 3: 295-303.

Conrac Corp., Conrac Division. 1980. Raster Graphics Handbook. Covina, California: Conrac Corp.

Daily, Mike. 1983. “Hue-Saturation-Intensity Split-Spectrum Processing of Seasat Radar Imagery.” Photogrammetric Engineering& Remote Sensing, Vol. 49, No. 3: 349-355.

ERDAS 2000. ArcView Image Analysis. Atlanta, Georgia: ERDAS, Inc.

ERDAS 1999. Field Guide. 5th ed. Atlanta: ERDAS, Inc.

ESRI 1992. Map Projections & Coordinate Management: Concepts and Procedures. Redlands, California: ESRI, Inc.

Faust, Nickolas L. 1989. “Image Enhancement.” Volume 20, Supplement 5 of Encyclopedia of Computer Science and Technology, edited by Allen Kent and James G. Williams. New York: Marcel Dekker, Inc.

Gonzalez, Rafael C., and Paul Wintz. 1977. Digital Image Processing. Reading, Massachusetts: Addison-Wesley Publishing Company.

Holcomb, Derrold W. 1993. “Merging Radar and VIS/IR Imagery.” Paper submitted to the 1993 ERIM Conference, Pasadena, California.

Hord, R. Michael. 1982. Digital Image Processing of Remotely Sensed Data. New York. Academic Press.

218 References

Jensen, John R., et al. 1983. “Urban/Suburban Land Use Analysis.” Chapter 30 in Manual of Remote Sensing, edited by Robert N. Colwell. Falls Church, Virginia: American Society of Photogrammetry.

Jensen, John R. 1996. Introductory Digital Image Processing: A Remote Sensing Perspective. Englewood Cliffs, New Jersey: Prentice-Hall.

Kloer, Brian R. 1994. “Hybrid Parametric/Non-parametric Image Classification.” Paper presented at the ACSM-ASPRS Annual Convention, April 1994, Reno, Nevada.

Lillesand, Thomas M., and Ralph W. Kiefer. 1987. Remote Sensing and Image Interpretation. New York: John Wiley & Sons, Inc.

Marble, Duane F. 1990. “Geographic Information Systems: An Overview.” Introductory Readings in Geographic Information Systems, edited by Donna J. Peuquet and Duane F. Marble. Bristol, Pennsylvania: Taylor & Francis, Inc.

McCoy, Jill, and Kevin Johnston. Using ArcGIS Spatial Analyst. Redlands, California: ESRI, Inc.

Sabins, Floyd F., Jr. 1987. Remote Sensing Principles and Interpretation. New York: W. H. Freeman and Co.

Schowengerdt, Robert A. 1983. Techniques for Image Processing and Classification in Remote Sensing. New York. Academic Press.

Schowengerdt, Robert A. 1980. “Reconstruction of Multispatial, Multispectral Image Data Using Spatial Frequency Content.” Photogrammetric Engineering & Remote Sensing, Vol. 46, No. 10: 1325-1334.

Star, Jeffrey, and John Estes. 1990. Geographic Information Systems: An Introduction. Englewood Cliffs, New Jersey: Prentice-Hall.

Swain, Philip H. 1973. Pattern Recognition: A Basis for Remote Sensing Data Analysis (LARS Information Note 111572). West Lafayette, Indiana: The Laboratory for Applications of Remote Sensing, Purdue University.

Swain, Philip H., and Shirley M. Davis. 1978. Remote Sensing: The Quantitative Approach. New York: McGraw Hill Book Company.

References 219

Tou, Julius T., and Rafael C. Gonzalez. 1974. Pattern Recognition Principles. Reading, Massachusetts: Addison-Wesley Publishing Company.

Tucker, Compton J. 1979. “Red and Photographic Infrared Linear Combinations for Monitoring Vegetation.” Remote Sensing of Environment, Vol. 8: 127-150.

Walker, Terri C., and Richard K. Miller. 1990. Geographic Information Systems: An Assessment of Technology, Applications, and Products. Madison, Georgia: SEAI Technical Publications.

Watson, David, 1994, Contouring: A Guide to the Analysis and Display of Spatial Data, Elsevier Science, New York.

Welch, R., and W.Ehlers. 1987. “Merging Multiresolution SPOT HRV and Landsat TM Data.” Photogrammetric Engineering & Remote Sensing, Vol. 53, No. 3: 301-303.

220 References

Index 235

Index

AA priori

defined 207Abstract symbol

defined 207Accuracy assessment

defined 207Advantages

bilinear interpolation 63cubic convolution 65nearest neighbor 64

American Standard Code for Information Inter-change

defined 207Analysis mask

defined 207Ancillary data

defined 207Annotation

defined 207AOI

defined 207Applying

data tools 51GeoCorrection tools 167Spectral enhancement 111

Arbitrary Affineformulas 200

Areadefined 207

Area of interestdefined 207

ASCIIdefined 208

Aspectdefined 208

Attributedefined 208

Averagedefined 208

BBand

defined 208

Bilinear interpolationadvantages and disadvantages 63defined 208Options dialog preferences 63

Bin functiondefined 208

Binsdefined 208

Borderdefined 208

Boundarydefined 208

Brightness inversionoverview 107

Brightness valuedefined 208

Brovey Transform 94Buffer zone

defined 209

CCamera

overview 191properties dialog

Camera tab 192Fiducials tab 193Orientation tab 191overview 191

Camera imageryorthorectification 43

Camera Modeltutorial 43

Camera propertiesdefined 209

Cartesiandefined 209

Categorizedefined 209

Celldefined 209

Cell sizedefined 209Options dialog 61workflow 68

Checkpoint analysisdefined 209overview 190

Chipping parametersoffset 199

236 Index

scale 199Circumcircle

defined 209Class

defined 209Class value

defined 209Classification

decision rules 157Mahalanobis distance 159maximum likelihood 158minimum distance 157nonparametric 149, 157overview 149Parallelepiped 160parametric 149, 157

defined 210enhanced data 151limiting dimensions 151nonparametric decision rule 149overview 147parametric decision rule 149process 148rectification 170scheme 150signatures 149supervised training 148supervised vs. unsupervised 151tips 150training 148unsupervised training 148

Classification accuracy tabledefined 210

Classification schemedefined 210

Clusteringdefined 210

Clustersdefined 210initial cluster means 153ISODATA 152overview 152

Coefficientdefined 210

Collinearitydefined 210

Color IR to Natural Coloroverview 120

ContactingERDAS 10

ESRI 10Contiguity analysis

defined 210Continuous

defined 210Continuous data

defined 210Contrast stretch

defined 211for display 99linear 98nonlinear 98overview 98piecewise linear 98varying it 99

Conversionoverview 162using 161

Convertingfeatures to raster 165raster to features 162

Convolutionexample 84filtering 84formula 85overview 84using 89workflow 88

Convolution filteringapplying 84defined 211

Convolution kerneldefined 211

Coordinate systemdefined 211

Correlation thresholddefined 211

Correlation windowsdefined 211

Corresponding GCPsdefined 211

Covariancedefined 211

Covariance matrixdefined 211

Create new imageoverview 72workflow 73

Creating a shapefiletutorial 19, 20

Index 237

Cubic convolutionadvantages and disadvantages 65defined 212Options dialog preferences 63

DData

defined 212Data file

defined 212Data file value

defined 212Data preparation

using 71Data tools

applying 51Data versus information 124Database

defined 212updating 4

Decision ruleclassification 157defined 212Mahalanobis distance 159maximum likelihood 158minimum distance 157nonparametric 149overview 149Parallelepiped 160parametric 149, 157

Decision rulesnonparametric 157

DEMdefined 212

Densitydefined 212

Digital elevation modeldefined 212

Digital terrain modeldefined 212

Dimensionalitydefined 213

Dimensionslimiting for classification 151

Disadvantagesbilinear interpolation 63cubic convolution 65nearest neighbor 64

Divergence

defined 213Diversity

defined 213DTM

defined 213

EEdge detector

defined 213Edge enhancer

defined 213Education solutions

ERDAS 10ESRI 10

Effects of orderpolynomial equation 182

Enhanced dataclassifying 151

Enhancementdefined 213linear 98nonlinear 98radiometric 97

Environmental hazardsidentifying 8mapping 8

ERDAScontacting 10education solutions 10

ESRIcontacting 10education solutions 10

Extensiondefined 213

Extent 59defined 213workflow 67

Extent tabOptions dialog 60

FFeature collection

defined 214Feature extraction

defined 214Feature space

defined 214Features to raster

238 Index

converting 165workflow 166

Fiducial centerdefined 214

Fiducialsdefined 214

File coordinatesdefined 214

Filteringdefined 214

Focaldefined 214

Focal analysisoverview 92using 94workflow 93

GGCP

defined 214GCP matching

defined 214GCPs

entering coordinates 170minimum number 187overview 169

General tabOptions dialog 59workflow 66

GeoCorrectiondefined 214overview 171properties dialog

Elevation tab 174General tab 171Links tab 172overview 171

Geocorrectiontutorial 43

GeoCorrection toolsapplying 167

Geographic databaseupdating 4

Geographic information systemdefined 215overview 123

Geoprocessingspecifying options 69tools 69

Geoprocessing modelsupdating 69

Georeferencingdefined 215only 169overview 169

GISdefined 123, 215

GIS analysisperforming 123

Ground control pointdefined 215

Ground control pointsoverview 169

HHelp

accessing for Image Analysis 10High-frequency kernel

defined 215overview 87

High-order polynomials 182nonlinear 182

Histogramdefined 215

Histogram equalizationdefined 215effect on contrast 104formula 103overview 101tutorial 14using 105workflow 104

Histogram matchingdefined 215overview 105using 107workflow 106

Huedefined 112, 215

IIHS to RGB

overview 115using 117workflow 116

IKONOSoverview 195

Index 239

properties dialogChipping tab 198Parameters tab 197

IKONOS propertiesdefined 216overview 195

Image algebra 118Image Analysis for ArcGIS

getting help 10Getting Started 12performing tasks 4quick-start tutorial 11

Image Analysis optionschanges for ArcGIS 69

Image datadefined 216

Image Differencetutorial 24workflow 142

Image differenceoverview 140

Image filedefined 216

Image infooverview 57

Image Info dialogworkflow 58

Image matchingdefined 216

Image processingdefined 216

Indicesdefined 216

Information versus data 124Infrared

defined 216Initial cluster means 153Intensity

defined 112IR

defined 216Island polygons


ISODATAclustering 152defined 216

Iterative Self-Organizing Data Analysis Tech-nique

defined 216

KKernels

high-frequency 87zero sum 86

LLand cover

categorizing 5Landsat

bands and wavelengths 201defined 217MSS 201number of satellites 200overview 200properties dialog

Parameters tab 205Landsat 7

data types 204satellite 204specifications 204

Layerdefined 217

Layer Stackoverview 143workflow 144

layer stack 144Linear

defined 217Linear contrast stretch 98

defined 217Linear transformation

defined 217Polynomial transformation 180Rubber Sheeting 189

Lookup tabledefined 217

Low-frequency kerneldefined 217example 87

LUTdefined 217

LUT Stretchoverview 98using 101workflow 100

240 Index

MMahalanobis distance rules

classification 159Majority

defined 217Map projection

defined 217Maximum

defined 218Maximum likelihood

defined 218Maximum likelihood rules

classification 158Mean

defined 218Median

defined 218Minimum

defined 218GCPs 187

Minimum distancedefined 218

Minimum distance rulesclassification 157

Minoritydefined 218

Modelingdefined 218

Mosaickingdefined 219overview 78tutorial 38workflow 80

MSSdefined 219Landsat 201

Multispectral classificationdefined 219

Multispectral imagerydefined 219

Multispectral scannerdefined 219

NNadir

defined 219National Imagery Transmission Format Stan-dard 196

Natural hazard damageidentifying 6summarizing 6

NDVIdefined 219

Nearest neighboradvantages and disadvantages 64defined 219Options dialog preferences 63

Neighborhood analysisdefined 219density 125diversity 125majority 125maximum 125minimum 125minority 125overview 125rank 125sum 126workflow 126

NITF 196NoData


Non-directionaldefined 219

Non-directional edgeoverview 89using 91workflow 91

Nonlineardefined 220

Nonlinear contrast stretch 98Nonlinear transformation

defined 220Polynomial transformation 181Rubber Sheeting 190

Nonparametric decision rule 149Parallelepiped 157

Nonparametric signaturedefined 220

Normalized difference vegetation indexdefined 220

OObservation

defined 220Off-nadir

Index 241

defined 220Offset

chipping parameters 199Options dialog

Cell Size tab workflow 68Extent tab workflow 67General tab workflow 66overview 59Preferences tab 68

Orthorectificationdefined 220overview 167rectification 171tutorial 43

Overlaydefined 220

PPanchromatic

SPOT 176Panchromatic imagery

defined 220Parallelepiped

defined 220rules classification 160

Parameterdefined 221

Parametric decision rulesMahalanobis distance 157Maximum likelihood 157minimum distance 157

Parametric ruleoverview 149

Parametric signaturedefined 221

Pattern recognitiondefined 221

PCAdefined 221

Performing GIS analysis 123Piecewise linear contrast stretch 98

defined 221Pixel

defined 221Pixel depth

defined 221Pixel size

defined 221Placing links

tutorial 48Polygon

defined 221Polynomial

defined 221Polynomial equation

effects of order 182Polynomial transformation

effects of order 182linear 180nonlinear 181overview 179transformation matrix 180

PreferencesOptions dialog 62, 68

Principal components analysisdefined 222

Principal pointdefined 222

Profiledefined 222

Pushbroomdefined 222scanner 176

QQuestions about Image Analysis

finding answers 9QuickBird

defined 222overview 195, 196properties dialog

Chipping tab 198Parameters tab 197

Quick-start tutorialImage Analysis for ArcGIS 11

RRadar data

defined 222Radiometric correction

defined 222Radiometric Enhancement

about 97Radiometric enhancement

defined 222Radiometric resolution

defined 223

242 Index

Rankdefined 223

Raster datadefined 223

Raster tab 65Raster to feature

converting 162workflow 164

Rational polynomial coefficientsdefined 223

Recodeby class name workflow 133by symbology workflow 135previously grouped image workflow 137

Recodingdefined 223

Rectificationclassification 170disadvantages 169georeferencing 169georeferencing only 169orthorectification 171overview 168RMS error 170thematic files 171triangle-based 189

Red, green, bluedefined 223

Reference coordinatesdefined 223

Reference pixelsdefined 223

Reference planedefined 224

Reprojectdefined 224

Reproject imageoverview 81workflow 81

Resamplingbilinear interpolation 63cubic convolution 63defined 224nearest neighbor 63

Rescale Imageoverview 145workflow 146

Resolutiondefined 224

Resolution merge

Brovey Transform 94defined 224overview 94using 96workflow 95

RGBdefined 224

RGB clusteringdefined 224

RGB to IHSoverview 112using 114workflow 114

RMS erroroverview 170tolerance 170

Root mean square errordefined 224

RPCdefined 224overview 196properties dialog

Chipping tab 198Parameters tab 197

RPC propertiesdefined 224overview 195

RSMEdefined 224tolerance of 170

Rubber Sheetingcheckpoint analysis 190defined 225Linear transformation 189nonlinear transformation 190overview 189triangle-based rectification 189triangulation 189

SSatellites

IKONOS 195Landsat 1-5 200Landsat 7 204QuickBird 196SPOT 176SPOT 4 178SPOT Panchromatic 176SPOT XS 177

Index 243

Saturationdefined 112, 225

Scalechipping parameters 199defined 225

Scannerdefined 225panchromatic 177pushbroom 176SPOT 176

Schemeclassification 150

Seed Radiusoverview 53workflow 56

Seed toolcontrolling 52defined 225properties overview 52workflow 53

Shadowenhancing 98

Shapefiledefined 225

Signaturedefined 225overview 149

Sitescharacterizing 5

Source coordinatesdefined 225

Spatial enhancementdefined 225overview 83

Spatial frequencydefined 225

Spatial resolutiondefined 225

Speckle noisedefined 225

Spectral distancedefined 226

Spectral enhancementapplying 111defined 226

Spectral resolutiondefined 226

Spectral spacedefined 226

SPOT

defined 226Panchromatic 176pushbroom scanner 176satellite overview 176workflow 178XS 177

SPOT 4 satellite 178Standard deviation

defined 226Stereoscopic

imagery 177pairs 177

stretchlinear 98nonlinear 98

Subset Imageoverview 74

Subset image spectrallyworkflow 77

Subsettingdefined 226

Subsetting an image spatiallyworkflow 77

Sumdefined 227

Summarize areasoverview 129workflow 130

Supervised classificationoverview 155workflow 156

Supervised trainingdefined 227overview 148

Supervised vs. unsupervised classification151

Swath widthdefined 227

TTasks

performing in Image Analysis 4Temporal resolution

defined 227Terrain analysis

defined 227Terrain data

defined 227Thematic change

244 Index

defined 227overview 127tutorial 27workflow 128

Thematic datadefined 227

Thematic filesorthorectification 171rectification 171

Thematic mapdefined 227

Thematic mapperdefined 227

Themedefined 228

Thresholddefined 228

Tipsclassification 150

TMdefined 228displaying data in bands 203overview 201

Toolsapplying GeoCorrection 167

Trainingclassification 148defined 228signatures 149supervised 148unsupervised 148

Training sampledefined 228

Transformation matrixdefined 228Polynomial transformation 180

Transformationshigh-order polynomials 182linear 180, 189nonlinear 181, 190

Triangle-based rectification 189Triangulation

defined 228Rubber Sheeting 189

True colordefined 228

Tutorialexercises

adding images 14applying histogram stretch 14

finding areas of change 24getting started 12identifying similar areas 19mosaiking images 38orthorectification of camera imagery

43quick-start 11

UUnsupervised Classification

tutorial 29Unsupervised classification

clusters 152ISODATA clustering 152overview 152percentage unchanged 154pixel analysis 153workflow 155

Unsupervised trainingdefined 228overview 148

Unsupervised vs. supervised classification151

Urban growthidentifying changes 7

Usingconversion 161convolution 89data preparation 71focal analysis 94non-directional edge 91Resolution merge 96utilities 139

UtilitiesImage Difference 140Layer Stack 143Rescale Image 145using 139

VVariable

defined 228Vector data

defined 229Vegetative indices

applications 117defined 229examples 117

Index 245

overview 117Vegetative stress

identifying 9

XXS

SPOT 177

ZZero sum kernels 86Zoom

defined 229

246 Index

ImageAnalysisForArcGIS10_2010

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