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Spatio-spectral image analysis using classical and neural algorithms [electronic resource].

Published
Washington, D.C. : United States. Dept. of Energy, 1996.
Oak Ridge, Tenn. : Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy.
Physical Description
7 pages : digital, PDF file
Additional Creators
Los Alamos National Laboratory, United States. Department of Energy, and United States. Department of Energy. Office of Scientific and Technical Information
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Summary
Remote imaging at high spatial resolution has a number of environmental, industrial, and military applications. Analysis of high-resolution multi-spectral images usually involves either spectral analysis of single pixels in a multi- or hyper-spectral image or spatial analysis of multi-pixels in a panchromatic or monochromatic image. Although insufficient for some pattern recognition applications individually, the combination of spatial and spectral analytical techniques may allow the identification of more complex signatures that might not otherwise be manifested in the individual spatial or spectral domains. We report on some preliminary investigation of unsupervised classification methodologies (using both ``classical`` and ``neural`` algorithms) to identify potentially revealing features in these images. We apply dimension-reduction preprocessing to the images, duster, and compare the clusterings obtained by different algorithms. Our classification results are analyzed both visually and with a suite of objective, quantitative measures.
Report Numbers
E 1.99:la-ur--96-2866
E 1.99: conf-9611127--1
conf-9611127--1
la-ur--96-2866
Subject(s)
Other Subject(s)
Note
Published through SciTech Connect.
12/31/1996.
"la-ur--96-2866"
" conf-9611127--1"
"DE96014453"
ANNIE `96: artificial neural networks in engineering, St. Louis, MO (United States), 10-13 Nov 1996.
Roberts, S.; Theiler, J.; Gisler, G.R.
Funding Information
W-7405-ENG-36
View MARC record | catkey: 14143056