5.3-Image Classification

Report
Prepared by:
George McLeod
With support from:
NSF DUE-0903270
in partnership with:
Geospatial Technician Education Through Virginia’s Community Colleges (GTEVCC)
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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)
Supervised
 Unsupervised
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Image source: Dr. Ryan Jenson
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Requires minimal amount of input from user
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Based solely on numerical information in the data
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Matched by the analyst to information classes
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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
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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 multidimensional image space
This outputs a classified image
Image source: Dr. Ryan Jenson
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Used in supervised classification
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Homogeneous areas of land cover
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Information derived from:
field studies,
 thematic maps,
 other areas of knowledge
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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.
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Map digitizing
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On-screen digitizing
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Minimum Distance to the Means
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Maximum Likelihood
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.
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.
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Growth or shrinkage of urban areas
Deforestation of tropic areas
Fire and burn damage
Damage done by hurricanes,
earthquakes, and tornados
Phragmites
Autralis 1999
Phragmites
Autralis 2002
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http://glovis.usgs.gov/ USGS data viewer
http://edcsns17.cr.usgs.gov/NewEarthExplor
er/ 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

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