EFMANet: An Edge-Fused Multidimensional Attention Network for Remote Sensing Semantic Segmentation

Accurate semantic segmentation of remote sensing images is crucial for geographical studies. However, mainstream segmentation methods, primarily based on Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs), often fail to effectively capture edge features, leading to incomplete image feature representation and missing edge information. Moreover, existing approaches generally overlook the modeling of relationships between channel and spatial dimensions, restricting effective interactions and consequently limiting the comprehensiveness and diversity of feature representation. To address these issues, we propose an Edge-Fused Multidimensional Attention Network (EFMANet). Specifically, we employ the Sobel edge detection operator to obtain rich edge information and introduce an Edge Fusion Module (EFM) to fuse the downsampled features of the original and edge-detected images, thereby enhancing the model’s ability to represent edge features and surrounding pixels. Additionally, we propose a Multi-Dimensional Collaborative Fusion Attention (MCFA) Module to effectively model spatial and channel relationships through multi-dimensional feature fusion and integrate global and local information via an attention mechanism. Extensive comparative and ablation experiments on the Vaihingen and Potsdam datasets from the International Society for Photogrammetry and Remote Sensing (ISPRS), as well as the Land Cover Domain Adaptation (LoveDA) dataset, demonstrate that our proposed EFMANet achieves superior performance compared to existing state-of-the-art methods.