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Classification
GEOG370
Instructor: Christine Erlien
Overview
Classification
Reclassification
Buffers
Neighborhood functions, filters, & roving windows
Classification
A method of generalization Categorizing groups of objects Data grouped into classes according to some
common characteristics; reduces the number of data elements
Advantage: Reduction in # of data elements (& map complexity)
Disadvantage: Variation exists within a class
Classification
A good classification:– Classes are mutually exclusive (e.g., and
object will belong to one & only one class)– Classes are exhaustive (e.g., well-defined
enough so that need for “Other” category is eliminated)
– Serves a useful function
Classifications Binary (yes/no) simple
– Ex.: Forest/non-forest– Disadvantage: Significant within-group
variation (possibly > than between groups)– Solution: Establish more classes
• Issues– Graphic portrayal more complex– Boundaries
Equal interval, quartile, natural breaks, standard deviation
Classification: Land
Land classifications depend on the types of objects to group– Geological formations– Wetlands– Agriculture, land use, and land cover
Land Classifications
Anderson– Level I: Obtained from Landsat data– Level II: Obtained from high altitude aerial
photography– Level III: Obtained from medium altitude
aerial photography
Anderson Classification
Level I
1 Urban or Built-up Land
2 Agricultural Land
3 Rangeland
4 Forestland
5 Water
6 Wetland
7 Barren Land
8 Tundra
9 Perennial Ice and Snow
Level II
11 Residential
12 Commercial and Services
13 Industrial
14 Transportation, Communications, and Utilities
15 Industrial and Commercial Complexes
16 Mixed Urban or Built-up Land
17 Other Urban or Built-up Land
Land Classifications
National Land Cover Dataset (NLCD)– Modified version of Anderson classification
• Some level II classes consolidated• Level III of Anderson classification not
compatible with remote sensing resolution
Why standardize?
Useful in targeting a particular attribute of imagery
Example:
Reclassification
Land cover class Classification Reclassification
Forest 10 1
Water 11 0
Settlement 12 0
Agriculture 13 0
Reclassification0 1 1 0
0 0 1 0
0 0 0 0
0 1 0 1
0 2 0 0
0 2 2 0
0 0 0 0
0 0 0 0
0: black soil
1: red soil
0: forest
2: urban
+ =
0 3 1 0
0 2 3 0
0 0 0 0
0 1 0 1
Value Meaning
0
1
2
3
Black soil and forest
Forest on red soil
Urban on black soil
Red soil and urban
Solution: reclassify attribute values
Create an expression: [landuse]+[soil]
Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography
ReclassificationRaster Change the attribute codes
http://www.itc.nl/ilwis/applications/application07.asp
Reclassification
Original classification:Row crops (1-4): Corn, Potatoes, Vegetables, Other.
Grain crops (5-10): Oats, Barley, Rye, Wheat, Buckwheat, and Other.
5
62
3
1
Reclassification:1-4=>1 5-10=>2
Line dissolve: Lines that separate classes that are going to be combined will be removed
1
2
Vector Change entities & attributes; line dissolve
Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography
Reclassification
Various measurement levels Nominal Ordinal Interval/ratio
– Range-graded classifications: Grouping ranges of numerical values into classes
Buffers Create a zone of interest around an
entity Buffer: A polygon created through
reclassification at a specified distance from a point, line, or polygon.
Example: Point buffer
Finding stores within specified distance of an address
Graphic by Jun Liang, UNC-Chapel Hill, Department of Geography
Buffers
Example: Line buffer
To locate all houses within 1 mile of major highway
Example: Polygon buffer
To locate all factories within 10 miles of a city
Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography
BuffersDoughnut buffer: Multiple buffers around the same spatial object.
Setbacks: Area available to the city for lighting and utility work; measured from the center of a suburban street some distance into each property.
Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography
Buffers Variable buffer: Buffer based on friction,
barriers, or any other neighborhood functions; buffer width changes from one line segment to another.– Can be arbitrary, based on measurable
component of landscape, or mandated by law
100 meter influence
45 meter influence
150 meter influence
Graphic by Jun Liang, UNC-Chapel Hill, Department of Geography
Neighborhood Functions
Neighborhood function: GIS analytical function that operates on regions of the database within proximity of some starting point– Filter: A matrix of numbers used to modify
grid cell/pixel values of original data using mathematical procedures
Filter Types
High-pass filter: Enhances values that change rapidly from place to place; used to isolate edges– Directional filter: High pass filter that enhances
linear objects with a particular orientation
Low-pass filter: Emphasizes trends by eliminating unusual values through averaging
High Pass Filter
http://isis.astrogeology.usgs.gov/IsisWorkshop/Lessons/PowerSpatialFilters/FilterIntro/highpassfilter.html
Original 3x3 High Pass Filter Edges are sharp and small features stand out, while larger features are neutral.
7x7 High Pass Filter Edges are sharp and larger features have been enhanced, while the largest features are neutral.
Low-pass filter
http://rst.gsfc.nasa.gov/Sect1/Sect1_13.html
Roving window
From Demers (2005) Fundamentals of Geographic Information Systems
Roving window: High pass filter 41 45 45 44 45 45
40 45 43 41 43 42
39 44 44 42 40 40
41 43 44 39 39 43
35 40 39 37 43 40
38 38 36 34 35 35
-1 -1 -1
-1 9 -1
-1 -1 -1
31 60 53 45 56 71
26 64 37 23 48 47
18 57 55 45 31 32
44 53 59 17 20 53
26 66 43 44 62 57
7 52 41 21 79 49
Differences are enlarged.
Roving window: Low pass filter
Low-pass filter: Emphasizes trends by eliminating small pockets of unusual values. Low-pass filters generally serve to smooth the appearance of an image.
1/9 1/9 1/9
1/9 1/9 1/9
1/9 1/9 1/9
100 60 60 60
100 100 100 60
100 100 100 100
95 100 100 100
91 82 69 77
96 91 82 87
99 99 96 96
99 99 100 100
Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography
Directional pass filterDirectional pass filters (Edge detection filters): Designed to highlight linear features; can also be designed to enhance features which are oriented in specific directions.
Useful applications in geology, for the detection of linear geologic structures.
1/9 1/9 2
1/9 2 1/9
2 1/9 1/9
1/9 1/9 1/9
2 2 2
1/9 1/9 1/9
Can be used to detect east-west oriented linear objects.
Can be used to detect northeast-southwest oriented linear objects.
Graphics by Jun Liang, UNC-Chapel Hill, Department of Geography
Neighborhood Functions Focal function: Considers neighborhoods; the
output cell is the result of a calculation performed on a window of cells (kernel) around the cell of interest– e.g., filters
Block function: Performs a function that produces a block of cells with new values
Zonal function: Performs functions based on a group of cells with a common value (a zone).
Block function
From Demers (2005) Fundamentals of Geographic Information Systems
This example: Maximum
Other block function types:MajorityMinimumTotalAverageRangeStandard deviation
Zonal functions
http://courses.washington.edu/esrm590/lessons/raster_analysis1/index.html
Here, the zones are defined by the zone grid. The function is a zonal sum, which sums all the input cells per zone, and places the output in each corresponding zone cell in the output.
Focal function application
Mosaicking topographic quads to produce DEMs for watershed analysis
Quadrangle boundaries NoData values gaps in data
Focal mean function used to calculate values to assign to NoData cells
http://www.esri.com/news/arcuser/0701/moredem.html
Wrapping up