AGFNet: Adaptive Guided Scanning and Frequency-Enhanced Network for High-Resolution Remote Sensing Building Change Detection

Change detection in high-resolution remote sensing imagery is vital for applications such as urban expansion monitoring, land-use analysis, and disaster assessment. However, existing methods often underutilize the differential features of bi-temporal images and struggle with complex backgrounds, illumination variations, and pseudo-changes, which hinder accurate identification of true changes. To address these challenges, this paper proposes a Siamese change detection network that integrates an adaptive scanning state-space model with frequency-domain enhancement. The backbone is constructed using Visual State Space (VSS) Blocks, and a Cross-Spatial Guidance Attention (CSGA) module is designed to explicitly guide cross-temporal feature alignment, thereby enhancing the reliability of differential feature representation. Furthermore, a Frequency-guided Adaptive Difference Module (FADM) is developed to apply adaptive low-pass filtering, effectively suppressing textures, noise, illumination variations, and sensor discrepancies while reinforcing spatial-domain differences to emphasize true changes. Finally, a Dual-Stage Multi-Scale Residual Integrator (DS-MRI) is introduced, incorporating both VSS Blocks and the newly designed Attention-Guided State Space (AGSS) Blocks. Unlike fixed scanning mechanisms, AGSS dynamically generates scanning sequences guided by CSGA, enabling a task-adaptive and context-aware decoding strategy. Extensive experiments on three public datasets (LEVIR-CD, WHU-CD, and SYSU-CD) demonstrate that the proposed method surpasses mainstream approaches in both accuracy and efficiency, exhibiting superior robustness under complex backgrounds and in weak-change scenarios.