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Proceedings Paper

Neural processing of SAR imagery for enhanced target detection
Author(s): Allen M. Waxman; Carol H. Lazott; David A. Fay; Alan N. Gove; W. R. Steele
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Paper Abstract

Neural network models of early visual computation have been adapted for processing single polarization (VV channel) SAR imagery, in order to assess their potential for enhanced target detection. In particular, nonlinear center-surround shunting networks and multi-resolution boundary contour/feature contour system processing has been applied to a spotlight sequence of tactical targets imaged by the Lincoln ADT sensor at 1 ft resolution. We show how neural processing can modify the target and clutter statistics, thereby separating the poplulations more effectively. ROC performance curves indicating detection versus false alarm rate are presented, clearly showing the potential benefits of neural pre-processing of SAR imagery.

Paper Details

Date Published: 5 June 1995
PDF: 10 pages
Proc. SPIE 2487, Algorithms for Synthetic Aperture Radar Imagery II, (5 June 1995); doi: 10.1117/12.210839
Show Author Affiliations
Allen M. Waxman, MIT Lincoln Lab. (United States)
Carol H. Lazott, MIT Lincoln Lab. (United States)
David A. Fay, MIT Lincoln Lab. (United States)
Alan N. Gove, MIT Lincoln Lab. (United States)
W. R. Steele, MIT Lincoln Lab. (United States)

Published in SPIE Proceedings Vol. 2487:
Algorithms for Synthetic Aperture Radar Imagery II
Dominick A. Giglio, Editor(s)

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