Papers › Frequency-Temporal Attention Network for Remote Sensing Imagery Change Detection
Frequency-Temporal Attention Network for Remote Sensing Imagery Change Detection
Chunyan Yu; Haobo Li; Yabin Hu; Qiang Zhang; Meiping Song; Yulei Wang;
Change detection (CD) in remote sensing imagery is identified as a pivotal task in the field of Earth observation, while it usually confronts the dilemma of intricate data and minor alterations. To address the stated challenge, this letter presents an innovative frequency-temporal attention network for CD (FTAN), which incorporates two advanced modules including the multidimensional convolutional frequency attention module (MCFA) and the interactive attention module (IAM). Specifically, the MCFA module is essential for enhancing sensitivity in CD by merging multiscale spatial and frequency domain features. As a supplement to MCFA, the IAM aggregates category-related tokens and processes cross-attention information from different time phases. The seamless integration of MCFA and IAM empowers the FTAN network with enhanced capabilities to detect minor regions and edges accurately. Experiments on datasets like LEVIR-CD and DSIFN-CD demonstrate superior performance by outperforming existing models in F1 scores and IoU metrics. Our code and pretrained models will be released at https://github.com/chirsycy/FTAN .
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 | DSIFN-CD | FTAN | F1 | 89.56 | #2 of 9 | Archive leaderboard | report |
| Change Detection | DSIFN-CD | FTAN | IoU | 81.10 | #2 of 9 | Archive leaderboard | report |
| Change Detection | DSIFN-CD | FTAN | Precision | 90.54 | #2 of 9 | Archive leaderboard | report |
| Change Detection | DSIFN-CD | FTAN | Recall | 88.61 | #2 of 9 | Archive leaderboard | report |
| Change Detection | LEVIR-CD | FTAN | F1 | 90.51 | #23 of 28 | Archive leaderboard | report |
| Change Detection | LEVIR-CD | FTAN | IoU | 82.78 | #23 of 28 | Archive leaderboard | report |
| Change Detection | LEVIR-CD | FTAN | Precision | 92.41 | #23 of 28 | Archive leaderboard | report |
| Change Detection | LEVIR-CD | FTAN | Recall | 88.82 | #23 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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