Convolutional neural networks (CNN) and Transformers have made impressive progress in the field of remote sensing change detection (CD). However, both architectures have inherent shortcomings: CNN are constrained by a limited receptive field that may hinder their ability to capture broader spatial contexts, while Transformers are computationally intensive, making them costly to train and deploy on large datasets. Recently, the Mamba architecture, based on state space models, has shown remarkable performance in a series of natural language processing tasks, which can effectively compensate for the shortcomings of the above two architectures. In this paper, we explore for the first time the potential of the Mamba architecture for remote sensing CD tasks. We tailor the corresponding frameworks, called MambaBCD, MambaSCD, and MambaBDA, for binary change detection (BCD), semantic change detection (SCD), and building damage assessment (BDA), respectively. All three frameworks adopt the cutting-edge Visual Mamba architecture as the encoder, which allows full learning of global spatial contextual information from the input images. For the change decoder, which is available in all three architectures, we propose three spatio-temporal relationship modeling mechanisms, which can be naturally combined with the Mamba architecture and fully utilize its attribute to achieve spatio-temporal interaction of multi-temporal features, thereby obtaining accurate change information. On five benchmark datasets, our proposed frameworks outperform current CNN- and Transformer-based approaches without using any complex training strategies or tricks, fully demonstrating the potential of the Mamba architecture in CD tasks. Further experiments show that our architecture is quite robust to degraded data. The source code will be available in https://github.com/ChenHongruixuan/MambaCD

翻译：卷积神经网络（CNN）和Transformer在遥感变化检测领域已取得显著进展。然而，这两种架构均存在固有缺陷：CNN受限于有限的感受野，可能难以捕捉更广泛的空间上下文；而Transformer计算密集，在大型数据集上训练和部署成本高昂。最近，基于状态空间模型的Mamba架构在一系列自然语言处理任务中展现出卓越性能，能有效弥补上述两种架构的不足。本文首次探索了Mamba架构在遥感变化检测任务中的潜力。我们针对二值变化检测、语义变化检测和建筑物损毁评估任务，分别定制了相应框架，命名为MambaBCD、MambaSCD和MambaBDA。三个框架均采用前沿的Visual Mamba架构作为编码器，能够充分学习输入图像的全局空间上下文信息。对于三个架构共有的变化解码器，我们提出了三种时空关系建模机制，这些机制可与Mamba架构自然结合，并充分利用其特性实现多时相特征的时空交互，从而获取精确的变化信息。在五个基准数据集上，我们提出的框架在不使用任何复杂训练策略或技巧的情况下，超越了当前基于CNN和Transformer的方法，充分证明了Mamba架构在变化检测任务中的潜力。进一步实验表明，我们的架构对退化数据具有较强鲁棒性。源代码将发布于https://github.com/ChenHongruixuan/MambaCD。