Change detection (CD) by comparing two bi-temporal images is a crucial task in remote sensing. With the advantages of requiring no cumbersome labeled change information, unsupervised CD has attracted extensive attention in the community. However, existing unsupervised CD approaches rarely consider the seasonal and style differences incurred by the illumination and atmospheric conditions in multi-temporal images. To this end, we propose a change detection with domain shift setting for remote sensing images. Furthermore, we present a novel unsupervised CD method using a light-weight transformer, called UCDFormer. Specifically, a transformer-driven image translation composed of a light-weight transformer and a domain-specific affinity weight is first proposed to mitigate domain shift between two images with real-time efficiency. After image translation, we can generate the difference map between the translated before-event image and the original after-event image. Then, a novel reliable pixel extraction module is proposed to select significantly changed/unchanged pixel positions by fusing the pseudo change maps of fuzzy c-means clustering and adaptive threshold. Finally, a binary change map is obtained based on these selected pixel pairs and a binary classifier. Experimental results on different unsupervised CD tasks with seasonal and style changes demonstrate the effectiveness of the proposed UCDFormer. For example, compared with several other related methods, UCDFormer improves performance on the Kappa coefficient by more than 12\%. In addition, UCDFormer achieves excellent performance for earthquake-induced landslide detection when considering large-scale applications. The code is available at \url{https://github.com/zhu-xlab/UCDFormer}

翻译：通过比较两时相图像进行变化检测是遥感领域的一项关键任务。由于无需繁琐的标注变化信息，无监督变化检测方法已引起学界的广泛关注。然而，现有无监督变化检测方法极少考虑多时相图像中因光照和大气条件引起的季节性差异与风格差异。为此，我们提出一种面向遥感图像的域偏移变化检测设定。进一步，我们提出一种基于轻量级Transformer的新型无监督变化检测方法——UCDFormer。具体而言，首先提出一种由轻量级Transformer和域特定亲和权重构成的Transformer驱动图像翻译方法，以实时效率缓解两幅图像间的域偏移。完成图像翻译后，可生成翻译后的事件前图像与原始事件后图像之间的差异图。随后，提出一种新型可靠像素提取模块，通过融合模糊C均值聚类与自适应阈值的伪变化图，选择显著变化/未变化的像素位置。最终，基于所选像素对与二元分类器获得二值变化图。在具有季节与风格变化的不同无监督变化检测任务上的实验结果表明，所提UCDFormer方法具有有效性。例如，与若干相关方法相比，UCDFormer在Kappa系数上提升超过12%。此外，在大规模应用场景中，UCDFormer在地震诱发滑坡检测方面表现出优异性能。代码详见\url{https://github.com/zhu-xlab/UCDFormer}。