Polarimetric fusion for synthetic aperture radar target classification

摘要

The problem of target classification using Synthetic Aperture Radar (SAR) polarizations is considered from a Bayesian decision point of view. This problem is analogous to the multi-sensor problem. We investigate the optimum design of a data fusion structure given that each classifier makes a target classification decision for each polarimetric channel. Though the optimal structure is difficult to implement without complete statistical information, we show that significant performance gains can be made even without a perfect model. First, we analyze the problem from an optimal classification point of view using a simple classification example by outlining the relationship between classification and fusion. Then, we demonstrate the performance improvement on real SAR data by fusing the decisions from a Gram-Schmidt image classifier for each polarization. ¢ I997 Pattern Recognition Society. Published by Elsevier Science Ltd.