Robust Nonlocal Low-Rank SAR Time Series Despeckling Considering Speckle Correlation by Total Variation Regularization

Outliers and speckle both corrupt time series of synthetic aperture radar (SAR) acquisitions. Owing to the coherence between SAR acquisitions, their speckle can no longer be regarded as independent. In this study, we propose an algorithm for nonlocal low-rank time series despeckling, which is robust against outliers and also specifically addresses speckle correlation between acquisitions. By imposing total variation regularization on the signal's speckle component, the correlation between acquisitions can be identified, facilitating the extraction of outliers from unfiltered signals and the correlated speckle. This robustness against outliers also addresses matching errors and inaccuracies in the nonlocal similarity search. Such errors include mismatched data in the nonlocal estimation process, which degrade the denoising performance of conventional similarity-based filtering approaches. Multiple experiments on real and synthetic data assess the performance of the approach by comparing it with state-of-the-art methods. It provides filtering results of comparable quality but is not adversely affected by outliers. The source code is available at https://github.com/gbaier/nllrtv.