LAUSR

This letter proposes a robust framework for polarimetric covariance symmetries classification in synthetic aperture radar (SAR) images applying a prescreening on the data looks before they are used to perform inferences. More specifically, the devised method improves the performance of a previous work based on the exploitation of the special structures assumed by the covariance/coherence matrix when symmetric scattering mechanisms dominate the polarimetric returns. To do this, the algorithm selects first the most homogeneous data through the cancellation of those sharing the highest generalized inner product (GIP) values computed with the use of the geometric barycenters. Then, the procedure based on model order selection (MOS) developed in the homogeneous case is applied on the filtered data. The conducted tests show the potentiality of the proposed method in correctly classifying the observed scene of L-band real-recorded SAR data with respect to its standard counterpart.