SAR time series despeckling via nonlocal matrix decomposition in logarithm domain

Abstract

Advanced satellite and signal processing techniques have made Synthetic Aperture Radar (SAR) capable of monitoring the dynamic processes of the earth with high temporal frequency through co-registered time series. There are several challenges in interpreting these three-dimensional data cubes, including the inherent speckle effect and outliers caused by abrupt changes or sporadically appearing objects. In this paper, we propose a novel despeckling method, LogSAR-NLMD, based on nonlocal matrix decomposition in the logarithm domain to despeckle SAR time series without producing the artifacts caused by outliers. A maximum a posteriori (MAP) problem is developed by taking into account smoothness and low-rank prior knowledge in addition to the likelihood of SAR reflectivities following the Fisher-Tippett distribution. Additionally, nonlocal self-similarity is exploited to construct time series matrices that satisfy the prior better than the original data. In order to solve the MAP problem, the variable splitting procedure and the alternating direction method of multipliers (ADMM) framework are used. The proposed method has been validated by multiple experiments on synthetic and real data by comparing it with other state-of-the-art methods for despeckling SAR time series.