Seasonal-Aware Scale-Semantic Consistency Alignment Change Detection Network

Abstract. Change detection in remote sensing imagery is a crucial method for obtaining dynamic information about land cover. However, pseudo-changes caused by seasonal variations pose a significant challenge to detection accuracy. Seasonal variations, such as vegetation phenology and snow cover, introduce global appearance differences that are often mistaken for actual land cover changes. This phenomenon is particularly prominent in long-term monitoring tasks, where pseudo-changes dominate the detection results. Addressing the issues of global appearance differences and multi-scale feature fusion induced by seasonal changes, We propose a novel Seasonal-Aware Scale-Semantic Consistency Alignment Change Detection Network (SSCANet) for remote sensing image change detection. This approach incorporates a Seasonal-Aware Scale Alignment (ASA) module and a Seasonal-Aware Semantic Guided Fusion (SGF) module. By employing spatial scale transformation and semantic alignment, it reduces information mismatch in multi-scale feature fusion and enhances the perception of details in change regions. Experiments conducted on the GZ-CD and CDD datasets demonstrate that SSCANet achieves overall accuracy with F1 scores of 89.21% and 97.82%, with precision rates of 89.02% and 98.37%, respectively. These results represent significant improvements over other methods, demonstrating that SSCANet outperforms its counterparts in both overall accuracy and seasonal robustness. The findings confirm that this approach effectively suppresses seasonal false changes, enhancing the accuracy and reliability of change detection.