A Cross-Scale 3D Attention and Boundary-Aware Network for Sea-Land Segmentation

Coastal segmentation struggles with complex terrain configurations and discontinuous edge demarcation. To resolve these challenges, we propose a novel deep learning model, the Cross-Scale Attention and Boundary-Aware Network (CABANet). The network synergistically enhances segmentation performance through two core innovations. First, we design a Cross-Scale 3D Attention Fusion Module, which creatively stacks multi-scale 2D feature maps from the backbone into a pseudo-3D feature volume. It then performs efficient contextual modeling across different geographic scales via a decomposed 3D attention mechanism, generating a highly optimized semantic summary feature. Second, we design a boundary enhancement module to strengthen the extraction and analysis of boundary features and improve the segmentation of the sea-land boundary, while utilizing contextual information to guide the boundary enhancement process. Evaluations on Landsat-8 OLI and SLSD datasets confirm state-of-the-art performance in both regional homogeneity and edge continuity.