Effective Road Segmentation with Selective State-Space Model and Frequency Feature Compensation

Road segmentation from high-resolution remote sensing imagery is critical for tasks such as autonomous driving, urban planning, and geographic information systems. However, challenges such as intensity nonuniformity, pixel ambiguity, and the visual similarity between roads and natural features make accurate segmentation difficult. In this article, we propose a road segmentation framework built upon the Mamba architecture, integrating a novel frequency feature compensation (FFC) approach to improve segmentation performance. Specifically, we introduce a progressive FFC method, leveraging wavelet decomposition to capture fine-grained details by separating features into high- and low-frequency components. Multistage features extracted from the Mamba backbone are decomposed using this approach and progressively integrated to compensate for the essential details for accurate road segmentation. We also introduce a wavelet loss (WL) to improve the model's ability to capture fine structural variations in the frequency domain. Furthermore, we develop a spatial perception Mamba block (SPMB) to enhance the capture of spatial relationships. By seamlessly integrating global context and local structures with the selective state-space model and FFC, our framework significantly boosts road segmentation accuracy. Extensive experiments on three publicly available road segmentation datasets demonstrate that our method achieves state-of-the-art performance, surpassing existing approaches in segmenting complex roads.