Robustness to Spatially Correlated Speckle in Plug-and-Play PolSAR Despeckling

Synthetic aperture radar (SAR) provides valuable information about the Earth's surface in all-weather and day-and-night conditions. Due to the inherent presence of speckle phenomenon, a filtering step is often required to improve the performance of downstream tasks. In this article, we focus on dealing with the spatial correlations of speckle, which impacts negatively many of the existing speckle filters. Taking advantage of the flexibility of variational methods based on the plug-and-play (PnP) strategy, we propose to use a Gaussian denoiser trained to restore SAR scenes corrupted by colored Gaussian noise with correlation structures typical of a range of radar sensors. Our approach improves the robustness of PnP despeckling techniques. Experiments conducted on simulated and real polarimetric SAR images show that the proposed method removes speckle efficiently in the presence of spatial correlations without introducing artifacts, with a good level of detail preservation. Our method can be readily applied, without network re-training or fine-tuning, to filter SAR images from various sensors, acquisition modes (SAR, PolSAR, InSAR, PolInSAR), and spatial resolution. The code of the trained models is made freely available at (https://gitlab.telecom-paris.fr/ring/mulog-drunet).