MFA-SCDNet: A Semantic Change Detection Network for Visible and Infrared Image Pairs

Semantic Change Detection (SCD) in remote sensing imagery is a common technique for monitoring surface dynamics. However, geospatial data acquisition increasingly involves the collection of visible and infrared images. SCD in visible and infrared image pairs confronts the challenge of distinguishing genuine semantic change from spectral discrepancies caused by heterogeneous imaging mechanisms. To address this issue, we propose a Modal Feature Analysis Semantic Change Detection Network (MFA-SCDNet), a novel framework that analyzes cross-modal features for change identification. The proposed architecture operates through three principal technical components: An infrared feature enhancement module that transforms infrared inputs into three-channel representations through spectral domain adaptation, enhancing the network’s perception of both high-frequency and low-frequency information in images; an encoder–decoder structure that simultaneously extracts modality-specific features and common features through adversarial learning; and a synergistic information fusion mechanism that integrates semantic recognition with change detection through multi-task optimization. Specific features are employed for semantic recognition, while common features are utilized for change detection, ultimately resulting in a comprehensive understanding of semantic changes. Experiments on public datasets show that MFA-SCDNet has an average improvement of 9.4% in mIoU_bc and 12.9% in mIoU_sc compared with the alternatives. MFA-SCDNet has better performance in heterogeneous images SCD.