Comparison of three different detectors applied to synthetic aperture radar data

Kenneth I. Ranney; Hiralal Khatri; Lam H. Nguyen

doi:10.1117/12.478699

Translator Disclaimer

1 August 2002 Comparison of three different detectors applied to synthetic aperture radar data

Kenneth I. Ranney, Hiralal Khatri, Lam H. Nguyen

Author Affiliations +

Proceedings Volume 4727, Algorithms for Synthetic Aperture Radar Imagery IX; (2002) https://doi.org/10.1117/12.478699
Event: AeroSense 2002, 2002, Orlando, FL, United States

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).

Citation Download Citation

Kenneth I. Ranney, Hiralal Khatri, and Lam H. Nguyen "Comparison of three different detectors applied to synthetic aperture radar data", Proc. SPIE 4727, Algorithms for Synthetic Aperture Radar Imagery IX, (1 August 2002); https://doi.org/10.1117/12.478699

ACCESS THE FULL ARTICLE