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. 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. Specifically, we obtained 83.11%, 88.39% and 94.19% F1 scores on the three BCD datasets SYSU, LEVIR-CD+, and WHU-CD; on the SCD dataset SECOND, we obtained 24.11% SeK; and on the BDA dataset xBD, we obtained 81.41% overall F1 score. 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）与Transformer已在遥感变化检测（CD）领域取得显著进展，但两种架构均存在固有缺陷。近期，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，可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CD任务中的潜力，针对二值变化检测（BCD）、语义变化检测（SCD）和建筑物损毁评估（BDA）分别定制了相应框架——MambaBCD、MambaSCD和MambaBDA。三种框架均采用前沿的Visual Mamba架构作为编码器，能够从输入图像中充分学习全局空间上下文信息。对于三种架构中通用的变化解码器，我们提出了三种时空关系建模机制，可自然融入Mamba架构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，所提框架无需采用任何复杂训练策略或技巧，即优于当前基于CNN和Transformer的方法，充分展示了Mamba架构在CD任务中的潜力。具体而言，我们在三个BCD数据集（SYSU、LEVIR-CD+和WHU-CD）上分别获得83.11%、88.39%和94.19%的F1分数；在SCD数据集SECOND上获得24.11%的SeK指标；在BDA数据集xBD上获得81.41%的整体F1分数。进一步实验表明，本架构对退化数据具有极强的鲁棒性。源代码将在https://github.com/ChenHongruixuan/MambaCD 公布。