Introduction to Imagery and Raster Data in ArcGIS · 2012-08-20 · Adding Imagery and Raster Data...
Transcript of Introduction to Imagery and Raster Data in ArcGIS · 2012-08-20 · Adding Imagery and Raster Data...
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Technical Workshops |
Esri International User Conference San Diego, California
Introduction to Imagery and Raster Data in ArcGIS
Simon Woo – slides Cody Benkelman - demos
July 25, 2012
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• Varieties - types of rasters • Raster properties • Display Raster Data in ArcMap • Display a Mosaic Dataset • Process Raster Data in ArcGIS • ArcGIS 10.1 improvements • Questions / Comments
Overview of Presentation
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Varieties of Imagery and Raster Data Aerial Imagery
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Varieties of Imagery and Raster Data Satellite Imagery
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Varieties of Imagery and Raster Data Elevation / Hillshade
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Varieties of Imagery and Raster Data Scanned Maps or Base Maps
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Varieties of Imagery and Raster Data MD representation of lidar and terrain
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Varieties of Imagery and Raster Data Pictures or Graphics
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Adding Imagery and Raster Data
• File on disk
• Geodatabase
Mosaic dataset
• Introduced at Version 10 • Images remain in original formats • Metadata is stored in attributes
• Able to manage large collections
Raster product
• Introduced at Version 10.1 • Able to display raw sensor files • Rendered with templates if possible
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Adding Imagery and Raster Data
ArcGIS Online Map Service
Image Service WCS / WMS Service
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Cody Benkelman
Using Raster Data Demo
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• Data source - Type of file and location
• Raster information - Information about the pixels
• Extent - Top, bottom, left and right extents
• Spatial reference - Coordinate system information
• Statistics - Min, max, mean, and standard deviation (per band)
Raster Properties
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• Extent = top, right, bottom, and left minimum bounding rectangle
• Spatial reference = projection
Extent + Spatial Reference = Geographic location
Y
X
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• Multiple resolution dataset layers of the original raster
• Improves display performance - Uses closest resolution level,
then resampled data is displayed
• Adds additional storage - But can now be compressed
• Pyramids are not used during analysis
Raster Pyramids
2x2 pixel block
1 meter
1st (2 m)
2nd (4 m)
3rd (8 m)
Zoom
out
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• Calculates the minimum, maximum, mean, and standard deviation for each band
• Used in applying a contrast stretch, classifying data, and color correction.
Statistics
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• Pixel or Bit depth - determines the possible range of values stored in each band.
- A depth of eight will store 28 = 256 values (0 to 255), - A depth of 16 will store 216 = 65,536 values (0 to 65,535).
• Unsigned – only positive values
• Signed – negative and positive values
Pixel Depth
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• Cells or pixels that do not have data values
- NoData and "0" are not always the same.
- "0" is a valid value
• Storage - A value for file-based raster - A bit mask for ArcSDE, and file-GDB
rasters
• NoData does not participate in statistics calculation
NoData
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Cody Benkelman
Raster Properties Demo
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• Renderers - Display your data with a renderer that makes your data look
good
• Image Analysis window
- Common capabilities in one easy to access location
Displaying Raster Data
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• Often used for elevation, satellite and aerial imagery • Default: when raster has more than 25 unique values • Stretches values along a color ramp
Stretched renderer
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• Stretches values along a color ramp • Uses a contrast stretch
Stretched renderer
Histogram (Before) Histogram (After)
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• Often used for satellite imagery and aerial photos • Default: Raster has 3 or more bands
- Displays each band through a different color (Red, Green, and Blue)
RGB renderer
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• Often used for land use and scanned maps • Default: Raster has fewer than 25 unique values
- Uses random colors for individual values
Unique Values renderer
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• Often used for land use and soil maps for consistency • Default: Colormap is present
- Uses pre-chosen colors for individual values
Colormap renderer
6 =
1 = = 255 , 255 , 50 255 , 255 , 50= 0 = 0 , 0 , 175
3 = = 0 , 0 , 175 0 , 0 , 17757555 = 255 , 175 , 20 3 == =
4 = = 255 , 175 , 20200 = 135 , 90 , 0 4 ===
5 = = 135 , 90 , 00 0 = 120 , 215 , 0 120 , 215 , 00 = 0 , 100 , 15 6 ===
7 = = 0 , 100 , 155 = 100, 220 , 255
1 =
3
== 2 =
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• Often used for grouping data values• Not a default renderer; can be used for single band data • Places ranges of pixels into separate categories
Classified renderer
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• Common rendering and processing tools to make your imagery work easier and faster
• You are able to change the following display parameters:
- effects tools - symbology tools (gamma level, DRA,
stretch, etc) - choose the resampling method - accelerate raster
Image Analysis window
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Cody Benkelman
Raster Display Demo
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• Quick Catalog - All raster datasets - Imagery from different sensors
• Create – In Geodatabase - Metadata - Processing to be applied - Default viewing rules
• Access – Any ArcGIS application or as service • As Image
- Dynamic Mosaic , Processed on the fly
• As Catalog - Footprints, Detailed metadata
Mosaic Dataset Optimum Model for Image Data Management
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• Imagery processed as accessed • Processes
- Stretch, Extract Bands - Clip, Mask - Reproject, Orthorectify - Pan Sharpen, Color Correction - Vegetation Index, Classify - Shaded Relief, Slope, Aspect
• Applied to - Individual rasters in mosaic - Compete Mosaic Dataset
On-The-Fly Processing Create Multiple Products from a Single Source
Utilizing the full image information content
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• Control of Mosaic Method (Manager/User) - By Date –’Latest’, ‘Closest to May 2001’ - By Attribute – ‘Highest Sun Angle’ - By Viewpoint – North, South, East, West - Seamline – Feathered blend
• Queries possible – ‘Landsat, no clouds, later than June 2001’
- Display “best” available imagery
Dynamic Mosaicking Control which images to display
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• Footprint view - View the minimum bounding rectangle for each raster
Mosaic Dataset rendering
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• Rendering pixels – similar to a raster dataset - Level of detail – like scale dependency - Overviews – display rasters quickly at all resolutions
Mosaic Dataset rendering
1 : 100 000
1 : 25 000
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Cody Benkelman
Mosaic Dataset Demo
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• Combining bands • Clipping • Mosaicking • Pansharpening • Orthorectifying • …
Processing raster data in ArcGIS
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• Combine many images into a multi-band raster
• Input bands can be from a single or multiple band raster dataset
• Composite with geoprocessing tool, or Image Analysis window.
Combining Bands
Composite Bands
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• Clip a portion of raster to fit your study area • Clip with geoprocessing tool, or Image Analysis window.
Clipping
Clip
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• Combine two or more adjacent and overlapping rasters together
• Mosaic with geoprocessing tool, or Image Analysis window
Mosaicking
Mosaic
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• Fuse a low resolution RGB image with a high resolution panchromatic image
- Output is a high resolution color image
• Pansharpen with geoprocessing tool, or Image Analysis window
Pansharpening
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• Display an image with more accuracy - Requires an image with sensor model and an elevation
source
• Orthorectify with geoprocessing tool, or Image Analysis window
Orthorectify
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Image Analysis window
Clip
Export data
Composite bands
NDVI
Colormap to RGB Difference
Pan-sharpening Orthorectify
Mask Shaded relief
Mosaic Filter
Add Function
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Cody Benkelman
Raster Processing
Demo
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• More raster formats (16) • Raster products • Image Analysis window updated • Automatic rendering based on data type • New GP tools (8) • Batch editing functions • New raster functions (5) • Improvements for the mosaic dataset • Auto Georeferencing
What is new in ArcGIS 10.1
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Recommended Raster Sessions
Using World Elevation services
Creating a mosaic dataset
Thursday 3:15pm – 3:35pm – 1A
Managing imagery and raster data using mosaic datasets
Wednesday 10am-10:30am Imagery Demo Theater
Improved georeferencing workflows in 10.1
Wednesday 3:15 pm– 4:30pm – 15A
Wednesday 11am -11:30am Imagery Demo Theater
Thursday 1:30 pm– 2:45pm – 15A
Thursday 4:05pm – 4:25pm
Understanding Raster Functions
Wednesday 10:30am -11:00am Imagery Demo Theater
W d d 11 11 30
Raster Products – Making imagery easier
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Please fill out your user evaluations. Thank you!
Imagery Resource Center http://resources.arcgis.com/en/communities/imagery/
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Recommended Raster Sessions
Tuesday 3:15PM – 15A Wednesday 8:30AM – 15A
Managing imagery and raster using mosaic datasets (75min)
Tuesday 9:300AM Thursday 9:00AM GDB Island
Wednesday 8:55AM – 6A
Image processing in ArcGIS (20min)
Tuesday 3:15PM – 14A Wednesday 1:30PM – 14A
Sharing imagery and raster data in ArcGIS (75min)
Tuesday 4:00AM Wednesday 3:30AM GDB Island
Thursday 12:00PM Imagery Island
Tuesday 12:00PM Wednesday 9:00AM GDB Island
Wednesday 10:00AM Web & Server Island
Tuesday 3:30AM Wednesday 11:00AM GDB Island
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