13 August 1999 ATR performance prediction using attributed scattering features
Author Affiliations +
We present a method for estimating classification performance of a model-based synthetic aperture radar (SAR) automatic target recognition (ATR) system. Target classification is performed by comparing a feature vector extracted from a measured SAR image chip with a feature vector predicted from a hypothesized target class and pose. The feature vectors are matched using a Bayes likelihood metric that incorporates uncertainty in both the predicted and extracted feature vectors. We adopt an attributed scattering center model for the SAR features. The scattering attributes characterize frequency and angle dependence of each scattering center in correspondence the geometry of its physical scattering mechanism. We develop two Bayes matchers that incorporate two different solutions to the problem of correspondence between predicted and extracted scattering centers. We quantify classification performance with respect to the number of scattering center features. We also present classification results when the matchers assume incorrect feature uncertainty statistics.
© (1999) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Hung-Chih Chiang, Hung-Chih Chiang, Randolph L. Moses, Randolph L. Moses, "ATR performance prediction using attributed scattering features", Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); doi: 10.1117/12.357693; https://doi.org/10.1117/12.357693

Back to Top