Non-stationary speckle reduction in high resolution SAR images

This paper attempts to address non-stationary speckle reduction in high-resolution synthetic aperture radar (HR-SAR) images, using a novel Bayesian approach. First, non-stationary speckle is defined. Second, an innovative log-normal mixture model (LogNMM) is proposed to model the underlying data; the data priors are represented by using Fields of Experts (FoE); and then the despeckling model is derived based on maximum a posteriori (MAP) method. The experimental results demonstrate that the proposal produces state-of-the-art despeckling performance on synthetic and real HR-SAR data, and prove that the speckle is non-stationary in the HR-SAR data of interest.