  Recently, the Mamba architecture based on state space models has demonstrated
remarkable performance in a series of natural language processing tasks and has
been rapidly applied to remote sensing change detection (CD) tasks. However,
most methods enhance the global receptive field by directly modifying the
scanning mode of Mamba, neglecting the crucial role that local information
plays in dense prediction tasks (e.g., CD). In this article, we propose a model
called CDMamba, which effectively combines global and local features for
handling CD tasks. Specifically, the Scaled Residual ConvMamba (SRCM) block is
proposed to utilize the ability of Mamba to extract global features and
convolution to enhance the local details, to alleviate the issue that current
Mamba-based methods lack detailed clues and are difficult to achieve fine
detection in dense prediction tasks. Furthermore, considering the
characteristics of bi-temporal feature interaction required for CD, the
Adaptive Global Local Guided Fusion (AGLGF) block is proposed to dynamically
facilitate the bi-temporal interaction guided by other temporal global/local
features. Our intuition is that more discriminative change features can be
acquired with the guidance of other temporal features. Extensive experiments on
three datasets demonstrate that our proposed CDMamba outperforms the current
state-of-the-art methods. Our code will be open-sourced at
https://github.com/zmoka-zht/CDMamba.
