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Change detection, a prominent research area in remote sensing, is pivotal inobserving and analyzing surface transformations. Despite significantadvancements achieved through deep learning-based methods, executinghigh-precision change detection in spatio-temporally complex remote sensingscenarios still presents a substantial challenge. The recent emergence offoundation models, with their powerful universality and generalizationcapabilities, offers potential solutions. However, bridging the gap of data andtasks remains a significant obstacle. In this paper, we introduce TimeTravelling Pixels (TTP), a novel approach that integrates the latent knowledgeof the SAM foundation model into change detection. This method effectivelyaddresses the domain shift in general knowledge transfer and the challenge ofexpressing homogeneous and heterogeneous characteristics of multi-temporalimages. The state-of-the-art results obtained on the LEVIR-CD underscore theefficacy of the TTP. The Code is available at \url{https://kychen.me/TTP}.

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