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A new technique, named diagonal polarimetric merge-using-moments (DPOL MUM), is proposed for the segmentation of multifrequency polarimetric synthetic aperture radar (SAR) images that exploits the characteristic block diagonal structure of their covariance matrix. This technique is based on the newly introduced split-merge test, which has a reduced fluctuation error than the straight extension of the polarimetric test (POL MUM) and is shown to yield a more accurate segmentation on simulated SAR images. DPOL MUM is especially useful in the extraction of information from urban areas that are characterized by the presence of different spectral and polarimetric characteristics. Its effectiveness is demonstrated by applying it to segment a set of SIR-C images of the town of Pavia. The classification of the image segmented with DPOL MUM shows higher probability of correct classification compared to POL MUM and to a similar technique that does not use the correlation properties (MT MUM)

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In this paper, we address the problem of deriving adequate detection and classification schemes to fully exploit the information available in a sequence of SAR images. In particular, we address the case of detecting a step reflectivity change pattern against a constant pattern. Initially we propose two different techniques, based on a Maximum Likelihood approach, that make different use of prior knowledge on the searched pattern. They process the whole sequence to achieve optimal discrimination capability between regions affected and not affected by a step change. The first technique (KSP-detector) assumes a complete knowledge of the pattern of change, while the second one (USP-detector) is based on the assumption of a totally unknown pattern. A fully analytical expression of the detection performances of both techniques is obtained, which shows the large improvement achievable using longer sequences instead of only two images. By comparing the two techniques it is also apparent that KSP achieves better performance, but the USP-detector is more robust. As a compromise solution, a third technique is then developed, assuming a partial knowledge of the pattern of change, and its performance is compared to the previous ones. The practical effectiveness of the technique on real data is shown by applying the USP-detector to a sequence of 10 ERS-1 SAR images of forest and agricultural areas, which is also used to validate the theoretical results