Self-Supervised Learning of Multi-Modal Cooperation for SAR Despeckling

Synthetic aperture radar (SAR) is a widely used modality for Earth observation, as they provide weather-independent imaging capabilities. However, interpretation of SAR images is difficult due to the speckle phenomenon: fluctuations appear in the image, which are stronger in areas with high radar reflectivity. As a result, many speckle reduction methods have been developed, with deep learning approaches standing out as particularly effective. Our article presents here a deep learning approach with two novel features: the use of an optical image to improve the restoration of a SAR image, while using a self-supervised neural network training.