Time Travelling Pixels: Bitemporal Features Integration with Foundation Model for Remote Sensing Image Change Detection
This addresses the problem of domain shift and multi-temporal image characteristics in remote sensing change detection, representing an incremental improvement with a novel method for a known bottleneck.
The paper tackles high-precision change detection in complex remote sensing scenarios by introducing Time Travelling Pixels (TTP), which integrates the SAM foundation model's latent knowledge, achieving state-of-the-art results on the LEVIR-CD dataset.
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 spatio-temporally 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. This method effectively addresses 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 is available at \url{https://kychen.me/TTP}.