Papers › ChangeCLIP: Remote sensing change detection with multimodal vision-language...

ChangeCLIP: Remote sensing change detection with multimodal vision-language representation learning

4 Jan 2024journal 2024 1archive 2025-07-28

Sijun Dong, Libo Wang, Bo Du, Xiaoliang Meng

Remote sensing change detection (RSCD), which aims to identify surface changes from bitemporal images, is significant for many applications, such as environmental protection and disaster monitoring. In the last decade, driven by the wave of artificial intelligence, many change detection methods based on deep learning emerged and have achieved essential breakthroughs. However, these methods pay more attention to visual representation learning while ignoring the potential of multimodal data. Recently, the foundation vision-language model, i.e. CLIP, has provided a new paradigm for multimodal AI, demonstrating impressive performance on downstream tasks. Following this trend, in this study, we introduce ChangeCLIP, a novel framework that leverages robust semantic information from image-text pairs, specifically tailored for Remote Sensing Change Detection (RSCD). Specifically, we reconstruct the original CLIP to extract bitemporal features and propose a novel differential features compensation module to capture the detailed semantic changes between them. Besides, we proposed a vision-language-driven decoder by combining the results of image-text encoding with the visual features of the decoding stage, thereby enhancing the image semantics. The proposed ChangeCLIP achieved state-of-the-art IoU on 5 well-known change detection datasets, LEVIR-CD (85.20%), LEVIR-CD+ (75.63%), WHUCD (90.15%), CDD (95.87%) and SYSU-CD (71.41%). The code and the pretrained models of ChangeCLIP will be publicly available on https://github.com/dyzy41/ChangeCLIP.

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Code

dyzy41/ChangeCLIP mentioned in paperpytorch report

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Tasks

Change DetectionDecoderLanguage ModellingRepresentation Learning

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Change Detection CDD Dataset (season-varying) ChangeCLIP F1 97.89 #4 of 18 Archive leaderboard report
Change Detection CDD Dataset (season-varying) ChangeCLIP F1-Score 97.89 #4 of 18 Archive leaderboard report
Change Detection CDD Dataset (season-varying) ChangeCLIP IoU 95.87 #4 of 18 Archive leaderboard report
Change Detection CDD Dataset (season-varying) ChangeCLIP Overall Accuracy 99.48 #4 of 18 Archive leaderboard report
Change Detection CDD Dataset (season-varying) ChangeCLIP Precision 98.02 #4 of 18 Archive leaderboard report
Change Detection CDD Dataset (season-varying) ChangeCLIP Recall 97.77 #4 of 18 Archive leaderboard report
Change Detection LEVIR-CD ChangeCLIP F1 92.01 #13 of 28 Archive leaderboard report
Change Detection LEVIR-CD ChangeCLIP IoU 85.20 #13 of 28 Archive leaderboard report
Change Detection LEVIR-CD ChangeCLIP Overall Accuracy 99.20 #13 of 28 Archive leaderboard report
Change Detection LEVIR-CD ChangeCLIP Precision 93.40 #13 of 28 Archive leaderboard report
Change Detection LEVIR-CD ChangeCLIP Recall 90.67 #13 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

CLIP

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