HFRU-Net: Hierarchical Feature Regulation with Attention-Guided Skip Connections and Spatial Context Enhancement for Road Extraction from Remote Sensing Images

Road extraction from high-resolution remote sesnsing images remains challenging due to complex backgrounds, diverse surface conditions, and the difficulty of maintaining road continuity. Existing U-Net-based methods mainly improve feature fusion strategies, but insufficient feature filtering and limited spatial structure representation often lead to background interference and discontinuous road segments. To address these issues, this paper proposes HFRU-Net, a U-Net-based hierarchical feature regulation framework incorporating an Attention Gate (AG) and a Spatially Enhanced Feed-Forward Network (SEFN). Specifically, AG is integrated into skip connections to adaptively suppress irrelevant background features and enhance road-related representations during multi-scale feature fusion. SEFN is embedded into decoder blocks to strengthen spatial context modeling through multi-scale feature interaction, thereby improving the detection of narrow roads and complex intersections. Extensive experiments on the DeepGlobe and WHU-RuR datasets demonstrate that HFRU-Net achieves mIoU values of 82.31% and 73.28%, respectively, outperforming several existing approaches. Ablation studies demonstrate performance improvements after introducing AG and SEFN, with AG contributing to feature regulation during cross-layer fusion and SEFN enhancing spatial context representation during decoding, while SEFN enhances spatial context representation during decoding, which may facilitate the recovery of narrow and spatially continuous road structures. Furthermore, cross-region evaluation on the GRSet dataset provides preliminary evidence of the transferability of the proposed method. The proposed HFRU-Net provides an effective framework for improving feature representation and spatial structure modeling in remote sensing road extraction.