working with rasters
DESCRIPTION
Working with Rasters. 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. Issue #1 - resolution. Larger cells: less precise spatial fix line + boundary thickening - PowerPoint PPT PresentationTRANSCRIPT
CS 128/ES 228 - Lecture 5a 1
Working with Rasters
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 - resolutionLarger 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 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
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 10
Analysis by raster overlay
Fig. 6.17, 3rd ed.
CS 128/ES 228 - Lecture 5a 11
Lack of spatial registration
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 tilesEx. 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 valuesHistogram 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 16
Raster data editing
CS 128/ES 228 - Lecture 5a 17
Clip to rectangle ...
CS 128/ES 228 - Lecture 5a 18
… vs. clip to shapefile
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)