Change Detection (CD) is a vital monitoring method in Earth observation, especially pertinent for land-use analysis, city management, and disaster damage assessment. However, in the era of constellation interconnection and air-sky collaboration, the changes in the Regions Of Interest (ROI) cause many false detections due to geometric perspective rotation and temporal style difference. In response to these challenges, we introduce CDNeXt, this framework elucidates a robust and efficient method for combining Siamese networks based on the pre-trained backbone with the innovative Temporospatial Interactive Attention Module (TIAM) for remote sensing imagery. The CDNeXt can be categorized into four primary components: Encoder, Interactor, Decoder, and Detector. Notably, the Interactor, powered by TIAM, queries and rebuilds spatial perspective dependencies and temporal style correlations from binary temporal features extracted by the Encoder to enlarge the difference of ROI change. Culminating the process, the Detector integrates the hierarchical features generated by the Decoder, subsequently producing a binary change mask. We have achieved new State-Of-The-Art (SOTA) performance in change detection, with our method surpassing existing techniques on four benchmark datasets: an F1 score of 82.63% on SYSU-CD, 87.14% on LEVIR-CD+, 66.71% on S2Looking, and 71.11% BANDON. To further validate the effectiveness of the TIAM, we compared it to other attention modules in both interactive and non-interactive modes. Our code is available on GitHub: .

中文翻译：

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基于时空交互注意力模块的遥感图像鲁棒变化检测

变化检测（CD）是地球观测中的重要监测方法，特别适用于土地利用分析、城市管理和灾害损失评估。然而，在星座互联和空天协作的时代，由于几何透视旋转和时间风格差异，感兴趣区域（ROI）的变化导致许多误检。为了应对这些挑战，我们引入了 CDNeXt，该框架阐明了一种稳健而有效的方法，将基于预训练主干的孪生网络与用于遥感图像的创新时空交互式注意模块（TIAM）相结合。 CDNeXt 可以分为四个主要组件：编码器、交互器、解码器和检测器。值得注意的是，由 TIAM 提供支持的 Interactor 从编码器提取的二进制时间特征中查询并重建空间视角依赖性和时间样式相关性，以放大 ROI 变化的差异。最终，检测器集成了解码器生成的分层特征，随后生成二进制变化掩码。我们在变化检测方面实现了新的最先进 (SOTA) 性能，我们的方法在四个基准数据集上超越了现有技术：SYSU-CD 上的 F1 分数为 82.63%，LEVIR-CD+ 上的 F1 分数为 87.14%，LEVIR-CD+ 上的 F1 分数为 66.71%在 S2Looking 上，以及 71.11% BANDON。为了进一步验证 TIAM 的有效性，我们将其与交互和非交互模式下的其他注意力模块进行了比较。我们的代码可以在 GitHub 上找到： 。