A Network for Merging SAR Image Sea-Land Segmentation and Coastline Detection Tasks

For the task of marine target detection in synthetic aperture radar (SAR) images, sea-land segmentation and coastline detection are often essential. Despite exciting results, many of them are still separately performed. Only a few studies have been done on the simultaneous realization of sea-land segmentation and coastline detection. To this end, this letter proposes a new network SAENet, wherein one edge enhancement module (EEM), one maximum fusion difference convolution (MaxFDC), and one multiscale spatial attention module (Multiscale SAM) are developed. In order to verify its effectiveness, we further construct one sea-land segmentation and coastline detection dataset with the Sentinel-1 ground range detected (GRD) data. The corresponding experimental results show that SAENet can reach 0.989 and 0.980 on the evaluation indexes $F1$ and IOU for sea-land segmentation, and 0.577 and 0.391 on the evaluation indexes ODS $F1$ and ODS IOU for coastline detection, which better accomplishes the task of sea-land segmentation and coastline detection simultaneously in comparison with other state-of-the-art (SOTA) methods.