Post on 17-Dec-2015
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
Fig. 3.10, 3rd ed.
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 bitB/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 8
Filtering raster data
Neighborhood averaging
Smoothes “holes” and transitions
Other techniques available
Chang 2002, p. 203
CS 128/ES 228 - Lecture 5a 9
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
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 13
Issue #4 – mosaicking rasters
http://www.microimages.com/featupd/v57/mosaic/
CS 128/ES 228 - Lecture 5a 14
Mosaicking: mismatched tiles
Ex. Aerial photographs of Kinzua Reservoir
What do you suppose caused the drastic differences in water clarity in the lake?
Google map of Onoville, NY. Accessed 6 Oct 2008
CS 128/ES 228 - Lecture 5a 15
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 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)