  This paper addresses the challenge of classifying polarimetric SAR images by
leveraging the peculiar characteristics of the polarimetric covariance matrix
(PCM). To this end, a general framework to solve a multiple hypothesis test is
introduced with the aim to detect and classify contextual spatial variations in
polarimetric SAR images. Specifically, under the null hypothesis, only an
unknown structure is assumed for data belonging to a 2-dimensional spatial
sliding window, whereas under each alternative hypothesis, data are partitioned
into subsets sharing different structures. The problem of partition estimation
is solved by resorting to hidden random variables representative of covariance
structure classes and the expectation-maximization algorithm. The effectiveness
of the proposed detection strategies is demonstrated on both simulated and real
polarimetric SAR data also in comparison with existing classification
algorithms.
