From Prior to Frequency: A Progressive Structure Recovery Network for Remote Sensing Road Segmentation

Roads in remote sensing imagery often contain narrow branches, complex boundaries, and long-range connections. Background interference and repeated downsampling make these structures difficult to preserve. To address these challenges, we propose a Progressive Structure Recovery Network (PSRNet) for remote sensing road segmentation. The network integrates structural-prior guidance, frequency-domain enhancement, and multiscale decoding. A line-structure prior is generated from the input image and encoded alongside hierarchical image features. Local Detail Alignment integrates prior cues with shallow features to enhance local structural details. Structure–Semantic Alignment combines deep semantic features with structural cues to refine unreliable prior responses. At the bottleneck, a Frequency Detail-Aware Enhancement module adaptively modulates the amplitude spectrum of the aligned features while preserving phase information. The reconstructed features are combined with spatial representations through residual fusion. A deep-to-shallow progressive decoder then restores spatial details and generates multiscale side predictions, which are fused to produce the final segmentation map. Experiments on the Massachusetts Roads, DeepGlobe, and CHN6-CUG datasets demonstrate the effectiveness of PSRNet for road segmentation. Ablation studies further support the complementary contributions of structural-prior guidance, frequency-domain enhancement, and progressive decoding.