Comparison of polarimetric SAR observables in terms of classification performance

The advent of fully polarimetric systems has led to an increased amount of information acquired by synthetic aperture radar (SAR) sensors but also to an increased complexity of the data to be analyzed and interpreted. In particular, the choice of several representations of the data, in terms of different parameters with peculiar characteristics and physical meaning, has been offered. With this work, we intend to address their systematic investigation with a twofold goal: 1) to provide a brief review of the polarimetric representations under consideration, 2) to characterize and compare them with respect to their usefulness for classification purposes. The analysis procedure consists of the accuracy estimation of classification tests performed on different parameters derived from L-band polarimetric SAR data. In order to assure a common basis for their comparison, a neural network classifier, the Multi-Layer Perceptron trained by the Back-Propagation learning rule, was used which permits to operate on the data without making any \emph{a priori} assumption on their statistics. In this way, the considered polarimetric parameters, in general characterized by different statistical distributions, may undergo the same classification process and the results compared. Our results indicate that the overall classification performance varies depending on the polarimetric parameters used. However, these variations are relatively limited and do not permit, at this stage, to define an ``absolute'' best representation to identify the classes under investigation in an optimal way.