CSFM-VMamba: integrating multiscale features and channel-spatial attention with Mamba for remote sensing image change detection

Remote sensing image change detection identifies surface changes by comparing images acquired at different time phases, offering crucial support for applications such as ecological monitoring, urban planning, disaster assessment, and resource management. However, the high resolution, complex noise, and diverse backgrounds of remote sensing images pose significant challenges, particularly in capturing subtle changes and mitigating noise interference. Existing methods often struggle with long-range dependency modeling, computational efficiency, and integrating shallow features and multiscale information, leading to suboptimal performance in detecting blurred boundaries and fine-grained changes. We propose the channel space fusion multiscale visual Mamba (CSFM-VMamba), an advanced change detection framework based on the visual Mamba architecture. CSFM-VMamba designs the channel space fusion block (CSFBlock) to jointly model channel and spatial attention, enabling flexible feature weight adjustments for more precise responses to changes. In addition, the multiscale selective fusion block (MSFBlock) facilitates dynamic fusion of multiscale features, enhancing sensitivity and robustness in detecting fine-grained changes. Comparative experiments on the SYSU-CD, LEVIR-CD+, WHU-CD, and SECOND datasets demonstrate that CSFM-VMamba consistently outperforms state-of-the-art methods, achieving performance improvements of 0.41% to 5.15% across various evaluation metrics. Ablation studies further validate the unique contributions of CSFBlock and MSFBlock, showcasing the potential of CSFM-VMamba in complex semantic change detection scenarios.