classification geog370 instructor: christine erlien

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


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


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.


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


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.


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

classification geog370 instructor: christine erlien

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