Optimum model-based segmentation techniques for multifrequency polarimetric SAR images of urban areas

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).