SLAFormer: Skeleton-Guided Large-Kernel Attention Transformer for Road Change Detection

Road change detection (RCD) is crucial for intelligent transportation, disaster assessment, and urban planning. However, current general change detection (CD) methods focus on various targets, such as buildings, while less attention is paid to the CD of narrow and elongated roads. Compared with general CD, RCD may still be limited by the following two aspects: On the one hand, the road usually occupies a small proportion of pixels in remote sensing images (RSIs) and is often easily blocked by buildings, trees, etc., making it difficult to ensure the integrity and connectivity of road structural features in RCD. On the other hand, RCD may easily be confused with the semantic information of similar material backgrounds (such as parking lots and building roofs) due to the lack of salient road features. To overcome the above limitations, we propose a skeleton-guided large-kernel attention Transformer (SLAFormer) for RCD, which can focus on salient road structural and semantic features to enhance its performance. In the proposed SLAFormer, we construct a novel skeleton-guided large-kernel attention module (SLKAM) and a frequency-guided cross spatial-channel difference module (FSCDM) to achieve the above goals. The SLKAM is used to make the model focus on road-specific skeleton features, which preserve the overall structure and morphology of roads to enhance the continuity and integrity of road features. In addition, the FSCDM is devised to better capture small-scale road changes and reduce semantic confusion with similar backgrounds, thereby enhancing change regions and extracting highly discriminative road difference information. Extensive experiments on two public RCD datasets show that ours achieves better RCD accuracy compared with several state-of-the-art (SOTA) approaches. The code will be available at https://github.com/TongfeiLiu/SLAFormer-for-RCD.