1 August 2002 Comparison of three different detectors applied to synthetic aperture radar data
Author Affiliations +
Proceedings Volume 4727, Algorithms for Synthetic Aperture Radar Imagery IX; (2002); doi: 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).
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Kenneth I. Ranney, Hiralal Khatri, 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); doi: 10.1117/12.478699; https://doi.org/10.1117/12.478699
PROCEEDINGS
9 PAGES


SHARE
KEYWORDS
Sensors

Detection and tracking algorithms

Synthetic aperture radar

Palladium

Neural networks

Target detection

Algorithm development

Back to Top