A new Bayesian source separation approach to blind decorrelation of SAR data

In this paper, a novel approach for performing blind decorrelation of SAR data is proposed. A patch-wise computation of the point-spread function (PSF) is performed directly from the SAR data to account for spatial nonstationarities present in SAR. The problem of estimating the PSF is formulated as an additive source separation problem in the frequency domain, and is subsequently solved using a Bayesian least squares estimation approach based on a Fisher-Tippett log-scatter model. Experimental results using both simulated SAR data and real RADARSAT-2 SAR sea-ice data showed that the proposed decorrelation approach can successfully learn the correct PSF and significantly reduce the correlation in SAR data.