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Proceedings Paper

SAR automatic target recognition using maximum likelihood template-based classifiers
Author(s): John A. Saghri
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Paper Abstract

A review of several recently-developed maximum likelihood template-based automatic target recognition (ATR) algorithms for extended targets in synthetic aperture radar (SAR) imagery data is presented. The algorithms are based on 'gradient' peaks, 'ceiling' peaks, edges, corners, shadows, and rectangular-fits. A weight-based Bayesian maximum likelihood scheme to combine multiple template-based classifiers is presented. The feature weights are derived from prior recognition accuracies, i.e., confidence levels, achieved by individual template-based classifiers. Application of feature-based weights instead of target specific feature-based weights reduces the resulting ATR accuracy by only a small amount. Preliminary results indicate that (1) the ceiling peaks provide the most target-discriminating power, (2) inclusion of more target-discriminating features leads to higher classification accuracy. Dempster-Shaffer rule of combination is suggested as a potential alternative to the implemented Bayesian decision theory approach to resolve conflicting reports from multiple template-based classifiers.

Paper Details

Date Published: 15 September 2008
PDF: 11 pages
Proc. SPIE 7073, Applications of Digital Image Processing XXXI, 70731I (15 September 2008); doi: 10.1117/12.799417
Show Author Affiliations
John A. Saghri, California Polytechnic State Univ. (United States)

Published in SPIE Proceedings Vol. 7073:
Applications of Digital Image Processing XXXI
Andrew G. Tescher, Editor(s)

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