Adaptive speckle MAP filtering for SAR images using statistical clustering

This paper presents a nonlinear adaptive filter based on the the maximum a posteriori (MAP) approach to reduce speckle in one-look, linear detected SAR images. The k-means clustering algorithm is combined with the MAP filter in order to cluster pixels with similar statistics (Changle Li's variance ratio). Assigned to each cluster there is a window size which is used to estimate the filter parameters. Several densities such as gaussian, gamma, chi-square, exponential, and Rayleigh were used as "a priori" model. To assess the improvement brought by the proposed algorithm we evaluate it with respect to edge preservation via Hough transform.