CS 128/ES 228 - Lecture 5a 1

Raster Formats (II)

CS 128/ES 228 - Lecture 5a 2

Spatial modeling in raster format

Basic entity is the cell

Region represented by a tiling of cells

Cell size = resolution

Attribute data linked to individual cells

CS 128/ES 228 - Lecture 5a 3

Issue #1 - resolution

Larger cells: less precise

spatial fix

line + boundary thickening

features too close overlap - less detail possible

CS 128/ES 228 - Lecture 5a 4

Why not always use tiny cells?

Data inputs may have limited spatial resolution - pixel size for aerial, satellite photos- reliability of coordinate measurements

Size of data files

Speed of analysis

CS 128/ES 228 - Lecture 5a 5

Issue #2 - determining cell values Data inputs may already

contain cell values: aerial, satellite photos

Cell values may be assigned: “pseudocolors”

Ultimately all cell values must be coded numerically

CS 128/ES 228 - Lecture 5a 6

Image depth minimum = 1 bit

B/W image or P/A data

8-bit image = 256 levels of gray (can be pseudo-colored)

24-bit image = true-color. Each primary color has separate layer

CS 128/ES 228 - Lecture 5a 7

Determining cell values

CS 128/ES 228 - Lecture 5a 8

Fuzzy set classification

CS 128/ES 228 - Lecture 5a 9

Filtering raster data

Neighborhood averaging

Smoothes “holes” and transitions

Other techniques available

Chang 2002, p. 203

CS 128/ES 228 - Lecture 5a 10

Additional attribute data

Some GISs provide a VAT linked to individual cells (e.g. ArcInfo GRID)

VAT data then accessible to database management system

CS 128/ES 228 - Lecture 5a 11

Issue #3 - layers in raster format

Each layer must be referenced in common coordinates

Thematic data can be combined and revised (reclassified)

CS 128/ES 228 - Lecture 5a 12

Analysis by raster overlay

CS 128/ES 228 - Lecture 5a 13

Lack of spatial registration

CS 128/ES 228 - Lecture 5a 14

Georeferencing raster images

Spatial coordinates may be absent or purely map coordinates (i.e. inches from one corner)

Control points: point features visible on both the image and the map

Linear or nonlinear transformations

“Rubber sheeting”

CS 128/ES 228 - Lecture 5a 15

Issue #4 – mosaicking rasters

http://www.microimages.com/featupd/v57/mosaic/

CS 128/ES 228 - Lecture 5a 16

Raster mosaicking: adjusting color values

Histogram matching:

Computer compiles histogram of color (or gray) values in 1 tile

2nd tile’s colors adjusted to match

CS 128/ES 228 - Lecture 5a 17

Raster mosaicking: matching edges

Matching edges:

Edge feathering

Cutline feathering

CS 128/ES 228 - Lecture 5a 18

Raster data editing

CS 128/ES 228 - Lecture 5a 19

Summary

A huge amount of spatial data are available in raster format

Rasters make excellent “base maps”

Easy to layer but watch coordinate systems!

Difficult/impossible to edit or reproject USGS Digital Raster Graphic (DRG) Quadrangle

(1:24,000 scale - UTM Zone 17, NAD 27)