Polarimetric SAR Despeckling by Integrating Stochastic Sampling and Contextual Patch Dissimilarity Exploration

Abstract

Speckle reduction has been the longstanding task since the invention of synthetic aperture radar (SAR). For further image analysis and interpretation, it usually demands better speckle suppression to preserve spatial and polarimetric information of the polarimetric SAR (PolSAR) images. In this paper, a new PolSAR filtering algorithm which combines stochastic sampling based on nonlocal mean, random walk model, and contextual patch dissimilarity is proposed. The nonlocal mean suppresses the speckle effectively while preserving the details and polarimetric information well. However, the size of nonlocal search window is fixed without taking the homogeneous and heterogeneous areas into consideration. Thus, it usually leads to computational redundancy. Correspondingly, we propose to apply the random walk to reduce the search domain. Contextual patch is also proposed to represent the large surroundings of a patch in a compact fashion. More precisely, the random walk model is utilized to determine the sampling path. Then, the traditional center patch dissimilarity and contextual patch dissimilarity are employed to measure the transition probability of the random walk sequence. Finally, the denoised estimation of PolSAR image is obtained by weight function derived from the transition probability. Since the stochastic sampling combines spatial random walk and the polarimetric dissimilarity measurements, the proposed algorithm fully exploits both the spatial and polarimetric information. Experimental results on synthetic and real PolSAR data demonstrate the effectiveness of the proposed method in terms of spatial and polarimetric information maintenance.