Geoinformatics Unit

Paper published in IEEE Transactions on Geoscience and Remote Sensing

April 29, 2020

Our paper entitled "Robust nonlocal low-rank SAR time series despeckling considering speckle correlation by total variation regularization" led by Gerald Baier has been published in IEEE Transactions on Geoscience and Remote Sensing.

Outliers and speckle both corrupt synthetic aperture radar (SAR) time series. Furthermore, due to the coherence between SAR acquisitions, their speckle can no longer be regarded as independent. We propose a nonlocal low-rank time series despeckling algorithm that is robust against outliers and also specifically addresses speckle correlation between acquisitions. By imposing a total variation regularization on the signal’s speckle component, its correlation between acquisition can be captured, facilitating the extraction of outliers from the unfiltered signal and correlated speckle. 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 denoising performance in conventional similarity-based filtering approaches. Multiple experiments on real and synthetic data assess the proposed approaches performance by comparing it to state-of-the-art methods. It provides filtering results of comparable quality but is not adversely affected by outliers.

Paper: https://ieeexplore.ieee.org/document/9079477
Code: https://github.com/gbaier/nllrtv

img12

Filtering results for the synthetic SAR stack of 48 images. DespecKS-NLLRTV is our proposed method.