Remote Sensing Change Detection by Pyramid Sequential Processing With Mamba

Change detection (CD) in remote sensing imagery is crucial for monitoring environmental variations over time. Recent advancements have introduced numerous effective algorithms, significantly enhancing the performance of CD tasks. However, various challenges still persist, such as complex backgrounds, small targets, and diverse change types. To address these challenges, we propose a novel Mamba-based multiscale feature enhancement method. Our method leverages the strengths of state space models in handling long sequences by reformulating the pyramid (multiscale) feature fusion as a sequence processing task. This approach enables our model to efficiently capture both long-range context and fine-grained local details, which are critical for effective feature learning and mitigating the aforementioned challenges. Moreover, we introduce scale-level embedding and auxiliary loss to optimize and regulate the model’s training process. These enhancements not only accelerate training but also improve the model’s generalization capability. Extensive experiments are conducted on four leading benchmarks: WHU-CD, SYSU-CD, LEVIR-CD, and DSIFN-CD datasets. The results demonstrate that our approach consistently delivers state-of-the-art performance, surpassing other cutting-edge methods by a significant margin.