Ratio-Based Multitemporal SAR Image Despeckling With Low-Rank Approximation

Synthetic aperture radar (SAR) has a wide range of applications in resource exploration, environmental monitoring, urban and rural planning, among others. However, SAR images often suffer from speckle noise, which requires the use of despeckling techniques. With the increasing availability of SAR time series, there is potential to develop more efficient despeckling methods. Nevertheless, in speckle reduction, the coherence between multitemporal SAR images creates new challenges. In this study, a patch-based low-rank approximation (PLRA) method is proposed for SAR time series despeckling using the RABASAR framework, which effectively eliminates temporal fluctuations and speckles. First, a similar patch search approach in time series is introduced to remove time-dependent changes by analyzing fluctuation models. Then, a low-rank approximation method based on patch stacks is proposed to obtain a low-rank image for noise filtering. Furthermore, the low-rank image is integrated into the RABASAR framework to improve the despeckling process. Experimental results demonstrate the superior performance of the proposed method in preserving image texture details, mitigating temporally correlated disturbances, and reducing speckle noise in comparison to other state-of-the-art methods.