Segmentation of Sentinel-1 SAR Images Over the Ocean, Preliminary Methods and Assessments

Abstract

Segmentations of ocean SAR images (Sentinel-1 A and B) into 10 classes of metoceanic phenomena are for the first time presented, with a 400 m resolution. Ocean SAR images segmentation differs from classic deep learning problems with a high variety of shapes and a particular importance of high-frequency patterns. To this end, an assessment of deep learning frameworks is performed, with a focus on the comparison between weakly supervised and supervised methods. Metrics based on the Wassertein distance indicate best performances by the supervised segmentation (U-Net) given operational constraints, thus highlighting the significance of properly annotated data sets. While available training data sets are made of small $20 \times 20 \text{km}$ imagettes, the extension of the inference from imagettes to wide swath images, with a wider variety of incidence angles, presents promising results and opens the way to more extensive oceanographic applications in SAR imagery.