提出3D-SSM模块，提升遥感变化检测的长程依赖捕捉能力。
3D-SSM: A Novel 3D Selective Scan Module for Remote Sensing Change Detection
- 设计3D选择性扫描模块，同时建模空间与通道维度全局信息。
- 在五个数据集上优于当前最优方法，显著提升细微变化检测精度。
- 适合关注遥感图像时序分析与多尺度特征融合的研究者。
现有基于Mamba的遥感变化检测方法虽提升了扫描能力，但仍难以有效捕捉图像通道间的长程依赖，制约了特征表达性能。为此，本文提出一种三维选择性扫描模块（3D-SSM），从空间平面和通道视角捕获全局信息，实现更全面的数据理解。基于该模块，设计两个核心组件：时空交互模块（SIM）通过跨时相图像的全局与局部特征交互，增强对细微变化的检测；多分支特征提取模块（MBFEM）融合频域、空域及3D-SSM特征，提供丰富的上下文表示。在五个基准数据集上的大量实验表明，所提方法优于当前先进水平。代码已开源：https://github.com/VerdantMist/3D-SSM。
原文摘要 · Abstract (English)
Existing Mamba-based approaches in remote sensing change detection have enhanced scanning models, yet remain limited by their inability to capture long-range dependencies between image channels effectively, which restricts their feature representation capabilities. To address this limitation, we propose a 3D selective scan module (3D-SSM) that captures global information from both the spatial plane and channel perspectives, enabling a more comprehensive understanding of the data.Based on the 3D-SSM, we present two key components: a spatiotemporal interaction module (SIM) and a multi-branch feature extraction module (MBFEM). The SIM facilitates bi-temporal feature integration by enabling interactions between global and local features across images from different time points, thereby enhancing the detection of subtle changes. Meanwhile, the MBFEM combines features from the frequency domain, spatial domain, and 3D-SSM to provide a rich representation of contextual information within the image. Our proposed method demonstrates favourable performance compared to state-of-the-art change detection methods on five benchmark datasets through extensive experiments. Code is available at https://github.com/VerdantMist/3D-SSM
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