CS 128/ES Lecture 5a1 Raster Formats (II)

CS 128/ES Lecture 5a2 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 Lecture 5a3 Effects of resolution – raster Larger cells:  less precise spatial fix  line + boundary thickening  features too close overlap - less detail possible

CS 128/ES Lecture 5a4 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 Lecture 5a5 Color as an attribute value Rasters from color photographs:  3 layers (Red Green Blue)  Typical values 0 – 255  Additive color wheel – displays  Subtractive color wheels – printing

CS 128/ES Lecture 5a6 Determining cell values

CS 128/ES Lecture 5a7 Fuzzy set classification

CS 128/ES Lecture 5a8 Raster data editing

CS 128/ES Lecture 5a9 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 Lecture 5a10 Layers in raster format Each layer must be referenced in common coordinates Thematic data can be combined and revised (reclassified)

CS 128/ES Lecture 5a11 Analysis by raster overlay

CS 128/ES Lecture 5a12 Georeferencing raster data Transformation parameters in a header (a.k.a. “World”) file Spatial coordinates assigned for 1 st cell Permits display of raster + vector data together Dimension of pixel in x-direction Rotation factor for row Rotation factor for column Dimension of pixel in y-direction x-coordinate of the center of upper-left pixel y-coordinate of the center of upper-left pixel Sample contents of a world file for ArcInfo or ArcView GIS

CS 128/ES Lecture 5a13 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 and nonlinear transformations possible

CS 128/ES Lecture 5a14 Lack of spatial registration

CS 128/ES Lecture 5a15 Georeferencing images in ArcView: Step 1

CS 128/ES Lecture 5a16 Georeferencing images in ArcView: Step 2

CS 128/ES Lecture 5a17 Affine transformations Translation Rotation Scaling Skew

CS 128/ES Lecture 5a18 Raster mosaicking: adjusting color values Histogram matching: Computer compiles histogram of color (or gray) values in 1 tile 2 nd tile’s colors adjusted to match

CS 128/ES Lecture 5a19 Raster mosaicking: matching edges Matching edges: Edge feathering Cutline feathering

CS 128/ES Lecture 5a20 Digitizing raster files Digitizing table high resolution (0.001”) either point or stream mode paper shrinkage/ expansion data registered to table coordinates – need to convert to map coordinates

CS 128/ES Lecture 5a21 How to digitize (a-b)

CS 128/ES Lecture 5a22 How to digitize (c-d)

CS 128/ES Lecture 5a23 “Heads up” digitizing Tracing on computer monitor: many scanned (raster) file formats supported poorer resolution, but uses less specialized equipment best for adding small # features or updating a file uses coordinate system of image or base map

CS 128/ES Lecture 5a24 Summary: Raster format A huge amount of spatial data are available in raster format Rasters are the format of choice for continuous features Rasters do a poor job of representing discrete features