Model-based Bayesian feature matching with application to synthetic aperture radar target recognition
We present a Bayesian approach for model-based classification from unordered, attributed feature sets. A set of features is estimated from measured data and is matched with a set predicted for each candidate hypothesis using a feature model. Both extracted and predicted feature sets have uncertainty, and some features may not be present in one set or the other. Computation of the match likelihoods requires a correspondence between estimated and predicted features, and two Bayesian correspondence methods are discussed. The proposed procedure is used to predict classification performance as a function of sensor parameters for a 10-vehicle target recognition problem using X-band synthetic aperture radar imagery. (C) 2001 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.
synthetic aperture radar target recognition hypothesis testing structural matching point correspondences performance estimation