Polarimetric classification of scattering centers using M-ary Bayesian decision rules

A Bayes-optimal decision rule is presented for detection and classification of scattering centers in clutter Scattering centers are modeled as one of M canonical reflectors with unknown amplitude, phase and orientation angle; clutter is modeled as a spherically invariant random vector. A choice of c osts in the Bayes risk is shown to yield a two-stage classification rule. T he first stage is a Neyman-Pearson detector which rejects clutter, whereas the second stage classifies the detection in one of the M target classes. T he resulting decision rule yields computationally simple implementation, in tuitive geometric interpretation, and posterior estimation of decision unce rtainty. Performance of the proposed classifier is illustrated on imagery f rom an airborne UHF-band radar.