Image Classification Basics
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Transcript of Image Classification Basics
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