An Approach for Integrating SAR Imagery in Sea-Land Segmentation and Coastline Detection

Segmentation of Synthetic Aperture Radar (SAR) imagery constitutes the cornerstone of SAR image analysis, with sea-land segmentation in SAR images playing a crucial role in determining the precision of subsequent sea surface target detection. This study introduces an integrated approach for sea-land segmentation and coastline detection in SAR imagery, aiming to overcome the limitations posed by the traditional separation of these two tasks. In essence, the proposed method merges a segmentation module and an edge detection module, employing a hollow convolution and a global context mechanism. Additionally, the approach utilizes a cross-entropy loss function incorporating multiple losses with adaptive weighting, thereby enhancing the richness of the extracted feature information. To validate the algorithm’s efficacy, a specialized dataset for sea-land segmentation and coastline detection is constructed, utilizing the GRD data format from the Sentinel-1 satellite. Experimental outcomes show that the presented algorithm achieves scores of 0.988 and 0.981 on the Intersection over Union (IOU) metrics for sea-land segmentation, and 0.569 and 0.401 on the Optimal Dataset Scale (ODS) F1 and ODS IOU metrics for coastline detection.