Papers › Time Travelling Pixels: Bitemporal Features Integration with Foundation Model for...
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
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}.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Change Detection | LEVIR-CD | TTP | F1 | 92.1 | #11 of 28 | Archive leaderboard | report |
| Change Detection | LEVIR-CD | TTP | IoU | 85.6 | #11 of 28 | Archive leaderboard | report |
| Change Detection | LEVIR-CD | TTP | Overall Accuracy | 99.2 | #11 of 28 | Archive leaderboard | report |
| Change Detection | LEVIR-CD | TTP | Precision | 93.0 | #11 of 28 | Archive leaderboard | report |
| Change Detection | LEVIR-CD | TTP | Recall | 91.7 | #11 of 28 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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