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Proceedings Paper

Comparison of three different detectors applied to synthetic aperture radar data
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Paper Abstract

The U.S. Army Research Laboratory has investigated the relative performance of three different target detection paradigms applied to foliage penetration (FOPEN) synthetic aperture radar (SAR) data. The three detectors - a quadratic polynomial discriminator (QPD), Bayesian neural network (BNN) and a support vector machine (SVM) - utilize a common collection of statistics (feature values) calculated from the fully polarimetric FOPEN data. We describe the parametric variations required as part of the algorithm optimizations, and we present the relative performance of the detectors in terms of probability of false alarm (Pfa) and probability of detection (Pd).

Paper Details

Date Published: 1 August 2002
PDF: 9 pages
Proc. SPIE 4727, Algorithms for Synthetic Aperture Radar Imagery IX, (1 August 2002); doi: 10.1117/12.478699
Show Author Affiliations
Kenneth I. Ranney, Army Research Lab. (United States)
Hiralal Khatri, Army Research Lab. (United States)
Lam H. Nguyen, Army Research Lab. (United States)

Published in SPIE Proceedings Vol. 4727:
Algorithms for Synthetic Aperture Radar Imagery IX
Edmund G. Zelnio, Editor(s)

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