Abstract: Due to complex factors, including illumination, seasons, phenology, solar height and angle changes, as well as the scattered and diverse nature of the target areas to be detected, along with the variability in scale and direction, remote sensing image change detection enfaces great technical challenges. In recent years, Transformer has demonstrated remarkable success in various fields such as natural language processing, object detection, and image segmentation, becoming a research focus. This paper reviews the latest research progress of Transformer-based remote sensing image change detection, and analyzes two types of methods based on pure transformer and convolutional neural Network (CNN) + Transformer. Then, a comparative analysis of the detection performance of different methods on public datasets is conducted, highliting the respective merits and limitations of various methods. Finally, future possible development trends are discussed.