Time Travelling Pixels: Bitemporal Features Integration with Foundation Model for Remote Sensing Image Change Detection

Keyan Chen, Chengyang Liu, Wenyuan Li, Zili Liu, Hao Chen, Haotian Zhang, Zhengxia Zou, Zhenwei Shi

Abstract: Change detection, a prominent research area in remote sensing, is pivotal in observing and analyzing surface transformations. Despite significant advancements achieved through deep learning-based methods, executing high-precision change detection in spatiotemporally complex remote sensing scenarios still presents a substantial challenge. The recent emergence of foundation models, with their powerful universality and generalization capabilities, offers potential solutions. However, bridging the gap of data and tasks remains a significant obstacle. In this paper, we introduce Time Travelling Pixels (TTP), a novel approach that integrates the latent knowledge of the SAM foundation model into change detection. TTP can effectively address the domain shift in general knowledge transfer and the challenge of expressing homogeneous and heterogeneous characteristics of multi-temporal images. The state-of-the-art results obtained on the LEVIR-CD underscore the efficacy of the TTP. The code has been made publicly available at https://github.com/KyanChen/TTP.