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Proceedings Paper

Performance comparison of neural network and statistical pattern recognition approaches to automatic target recognition of ground vehicles using SAR imagery
Author(s): Kevin M. Olson; Gary A. Ybarra
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Paper Abstract

Statistical and neural network approaches to the classification process of automatic target recognition (ATR) with a synthetic aperture radar (SAR) imaging mode for four ground vehicles are investigated and their performance compared. A set of image features is extracted from a training set of SAR images. A subset of these image features is selected which maximizes the likelihood of correct classification assuming a Gaussian feature distribution. An improved method for statistical classification is demonstrated in which training data is selected based on its statistical variation with azimuth angle. With proper selection of image features it is shown that the misclassification rates of both the statistical and neural network classifiers are approximately the same.

Paper Details

Date Published: 24 September 1997
PDF: 12 pages
Proc. SPIE 3161, Radar Processing, Technology, and Applications II, (24 September 1997); doi: 10.1117/12.279466
Show Author Affiliations
Kevin M. Olson, Mint Technology (United States)
Gary A. Ybarra, Duke Univ. (United States)


Published in SPIE Proceedings Vol. 3161:
Radar Processing, Technology, and Applications II
William J. Miceli, Editor(s)

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