Dual Category-Guided Spatial Mamba Network for Remote Sensing Semantic Segmentation

Abstract

With the rapid development of aerospace technology, remote sensing images contain increasingly rich feature information, while large variations in object scales and imbalanced category distributions pose great challenges to remote sensing semantic segmentation. Current mainstream methods often rely on attention mechanisms for global modeling, yet they tend to over-average features, making it difficult to capture minority categories and preserve fine-grained details. To address these limitations, we propose a Dual Category-Guided Spatial Mamba Network (DGSMNet) that combines dual category guidance with a spatial Mamba mechanism. Specifically, a Dual-Stage Category Guidance (DSCG) module is designed to extract class information via wavelet transforms and guide feature learning, ensuring proportional representation of all categories across channels and enabling the model to learn underrepresented classes. In addition, a Spatial Mamba (SPM) module is introduced to enhance long-range modeling while retaining sensitivity to local details. By integrating DSCG and SPM, the proposed DGSMNet improves category-aware representation and spatial modeling on the evaluated benchmarks. Extensive experiments on the ISPRS Vaihingen and Potsdam datasets demonstrate that DGSMNet achieves competitive performance among the compared methods.