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SAR classification and polarimetric fusion

The problem of target classification using synthetic aperture radar (SAR) polarizations is considered form 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. Thought 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 problem by outlining the relationship between classification and fusion. Then, we demonstrate the performance improvement by fusing the decisions from a Gram Schmidt image classifier for each polarization.