SAR despeckling based on soft classification

We propose a new approach to SAR despeckling, based on the combination of multiple alternative estimates of the same data. The many despeckling methods proposed in the literature possess different and often complementary strengths and weaknesses. Given a reliable pixel-wise classification of the image, one can take advantage of this diversity by selecting the more appropriate combination of estimators for each image region. We implement a simplified version of this approach, using soft classification and two state-of-the-art despeckling tools, with opposite properties, as basic estimators. Experiments on real-world high-resolution SAR images prove the effectiveness of the proposed technique and confirm the potential of the whole approach.