A Dual-Encoder Topology and Frequency-Aware Framework for Robust Road Extraction in High-Resolution Remote Sensing Images

Road extraction from high-resolution remote sensing imagery is essential for urban infrastructure monitoring, disaster management, and autonomous navigation. However, this task remains challenging because of limited generalization across domains, topological discontinuity under occlusion, and boundary blurring caused by loss of high-frequency information. To address these issues, this paper proposes a Frequency-Aware Dual-Encoder Network (FADENet) integrating a pre-trained SAM image encoder and D-LinkNet backbone, along with two novel modules: multi-directional Topology-Aware Aggregation (MTAA) for enhancing road connectivity in occluded regions and Adaptive Frequency-Aware Fusion (AFAF) for restoring boundary sharpness. Experiments on the HF road, DeepGlobe, and Massachusetts datasets demonstrate FADENet's superiority in key metrics, validating its effectiveness in improving road extraction completeness and precision in complex urban environments.