  Change detection is widely applied in remote sensing image analysis. Existing
methods require training models separately for each dataset, which leads to
poor domain generalization. Moreover, these methods rely heavily on large
amounts of high-quality pair-labelled data for training, which is expensive and
impractical. In this paper, we propose a multimodal contrastive learning
(ChangeCLIP) based on visual-language pre-training for change detection domain
generalization. Additionally, we propose a dynamic context optimization for
prompt learning. Meanwhile, to address the data dependency issue of existing
methods, we introduce a single-temporal and controllable AI-generated training
strategy (SAIN). This allows us to train the model using a large number of
single-temporal images without image pairs in the real world, achieving
excellent generalization. Extensive experiments on series of real change
detection datasets validate the superiority and strong generalization of
ChangeCLIP, outperforming state-of-the-art change detection methods. Code will
be available.
