IMAGE CLASSIFICATIONBASICS

With support from:

NSF DUE-0903270

in partnership with:

George McLeod

Prepared by:

Geospatial Technician Education Through Virginia’s Community Colleges (GTEVCC)

Image Analysis

Satellite images capture light by sampling over predetermined wavelength ranges which are referred to as “bands” or “channels”

To extract additional information from digital images use image processing techniques such as: False Color Composites, Image Ratios, and Classification (Supervised and Unsupervised)

Two Kinds of Classification

Supervised Unsupervised

Image source: Dr. Ryan Jenson

Unsupervised Classification

Requires minimal amount of input from user

Based solely on numerical information in the data

Matched by the analyst to information classes

Pixels with similar digital numbers are grouped together into spectral classes using statistical procedures such as cluster analysis ISODATA

Iterative Self-Organizing Data Analysis Technique - Automated spectral clustering

User then identifies which class membership for each cluster

ISODATA

Supervised Classification

User selects area in image that represent each unique class (“Training” sites)

Pixel values for each band are recorded for class sample set

Computer matches rest of pixels to user defined classes based on closest distance in multi-dimensional image space

This outputs a classified image

Supervised Classification – Training Sites

Supervised Classification – Signature Means

Supervised Classification

Image source: Dr. Ryan Jenson

Training Site Selection

Used in supervised classification

Homogeneous areas of land cover

Information derived from:field studies,thematic maps,other areas of knowledge

Training Site Selection (Cont.)

Each site should have at least 10 times ‘n’ number of pixels, where n is equal to the number of bands used in the classification.

Map digitizing

On-screen digitizing

Supervised Classification Algorithms

Minimum Distance to the Means

Maximum Likelihood

Minimum Distance to Means

The data points for DNs from two bands are dots; the mean for each clustered data set are the squares. For point 1, an unknown, the shortest straight-line distance to the several means is to the class "heather". Point 1, then, is assigned to this category. Point 2 is slightly closer to the "soil" category but lies within the edge of the "urban" spread. Here, the classification seems ambiguous. By the minimum distance rule, it would go to "soil" but this may be erroneous ("urban" would have been a greater likelihood). Point 3 is not near any of the class DN clusters, but is about equidistance between "urban", "water", "forest", and "heather". If one plays the odds, "urban" is just a tad closer to 3; but this situation indicates how misclassification might occur.

Maximum Likelihood

Not shown is the fact that inside each ellipse are contours that indicate the degree of probability. Associated with each ellipse is a separate plot that expresses a statistical surface (bell-shaped in three dimensions) called probability density functions. Using these functions, which relate to the contours, a likelihood that any unknown point U is most probably associated with some one ellipse is determined. A Bayesian Classifier is a special case in which the likely occurrence of each class (common to rare) is assessed and integrated into the decision making.

Supervised vs. Unsupervised

Land Cover/Land Use Change Analysis

Growth or shrinkage of urban areas Deforestation of tropic areas Fire and burn damage Damage done by hurricanes, earthquakes, and tornados

Land Cover Classification

Phragmites Autralis - 1999

Phragmites Autralis - 2002

Acquiring Satellite Data

http://glovis.usgs.gov/ USGS data viewer http://edcsns17.cr.usgs.gov/NewEarthExpl

orer/ USGS New Earth Explorer

http://www.landcover.org/index.shtml Global Landcover facility at the University of Maryland

http://www.terraserver.com/view.asp Terraserver

http://www.ncdc.noaa.gov/nexradinv/index.jsp NOAA NexRad Radar Data