Boundary-enhanced network for semantic segmentation of remote sensing images

Improving the accuracy of semantic segmentation for remote sensing images (RSIs) is crucial for the geoscientific research and applications. However, existing models tend to focus too much on the subject information for semantic objects, but less on the global-local boundary information of images. To tackle this issue, we introduce a novel approach named the Boundary Enhanced Network (BENet), which applies synchronized attention to both global and local aspects of images. Specifically, the proposed method utilizes the Global-Local Boundary Fusion module (GLBF) to aggregate the boundary information of the objects from a high-dimensional feature perspective. To adapt to the multi-scale characteristics of RSIs, GLBF is embed hierarchically to maintain boundary details across different stages. Besides, a Feature Enhancement Module (FEM) is devised with sequentially DEConv and Squeeze-and-Excitation (SE) blocks to refine the features from decoder. The experiments on the ISPRS Vaihingen and Potsdam datasets showed that BENet outperforms mainstream methodology, OA, mIoU and mF1 metrics were all significantly elevated.