MERLIN-Seg: Self-supervised despeckling for label-efficient semantic segmentation

Remote sensing satellites acquire a continuous stream of data on a daily basis. As most of those data are unlabeled, the development of algorithms requiring weak supervision is of paramount importance. In this paper, we show that the need for annotation for Synthetic Aperture Radar data can be reduced by coupling a despeckling task (self-supervised) and a segmentation task (supervised). The proposed self-supervised learning framework, called MERLIN-Seg, has been trained for building footprint extraction and achieves favorable performances even with 1% of annotated data. We show that conditioning the network on despeckling without labels is beneficial for supervised segmentation. Our experiments demonstrate that the joint training of the two tasks achieves better performances than a vanilla segmentation network in terms of IoU, F1 score, and accuracy on both simulated and real SAR images.