SAR Time-Series Despeckling via Nonlocal Total Variation Regularized Robust PCA

Zhanyu Zhu, Jian Kang, Teng-Yu Ji, Zhe Zhang, Rubén Fernández-Beltran

Abstract: Through the development of synthetic aperture radar (SAR) technology, it is now possible to observe dynamic processes on the earth with fine temporal resolution by forming SAR time series. Nonetheless, such sequential images remain difficult to interpret due to the speckle effect. Despeckling them is further complicated by outliers caused by abrupt changes in weather conditions or the appearance of objects. In spite of the fact that many state-of-the-art methods can achieve excellent filtering performances over stable areas, they often result in artifacts in those areas where outliers existed at the time of acquisition. To simultaneously mitigate the speckle noise and extract outliers, we propose a novel SAR time-series despeckling method based on nonlocal total variation (TV) regularized robust principle component analysis (RPCA), which is termed SAR-NL-TVRPCA. By comparing it to other state-of-the-art methods, the effectiveness of its despeckling has been validated in real data experiments. Furthermore, the extracted outliers can provide insights into abrupt changes occurring throughout the observation period, which provides byproducts for further analysis.