Improved Semantic Segmentation of High‐Resolution Remote Sensing Data: Utilizing a Novel Enhanced Boundary‐Aware Network for Efficient Processing

ABSTRACT With the recent advancements in deep learning and remote sensing technology, several semantic segmentation methods based on convolutional neural networks have been utilized for the semantic segmentation of remote sensing images. However, these models tend to lose valuable shallow detail information, particularly boundary information, due to feature compression during the encoding process. To address this issue, we propose an enhanced semantic segmentation model, EBANet, that focuses on boundary perception. EBANet consists of two parts—boundary path and spatial path, with boundary prediction serving as an independent subtask to enhance the segmentation model. Our model effectively aggregates boundary features and semantic features to achieve boundary enhancement segmentation. To achieve this, we employ a boundary filtering module (BFM) based on the gating mechanism that allows gating the low‐level boundary features through high‐level semantic understanding, thus effectively eliminating noisy data. The boundary‐guided contextual aggregation module (BCAM) enhances the interaction of boundary features with semantic features by establishing non‐local response relationships from boundary features to semantic features. We evaluated EBANet on high‐resolution remote sensing datasets Vaihingen and Potsdam, and the results showed that EBANet outperformed other models in terms of mIoU, F1‐Score, and OA, achieving the highest accuracy levels. On the Vaihingen dataset, EBANet's accuracy levels reached 68.31%, 81.05%, and 84.31%, respectively, while on the Potsdam dataset, it achieved 73.06%, 86.37%, and 89.64%, respectively.