Papers › DDLNet: Boosting Remote Sensing Change Detection with Dual-Domain Learning
DDLNet: Boosting Remote Sensing Change Detection with Dual-Domain Learning
Xiaowen Ma, Jiawei Yang, Rui Che, Huanting Zhang, Wei zhang
Remote sensing change detection (RSCD) aims to identify the changes of interest in a region by analyzing multi-temporal remote sensing images, and has an outstanding value for local development monitoring. Existing RSCD methods are devoted to contextual modeling in the spatial domain to enhance the changes of interest. Despite the satisfactory performance achieved, the lack of knowledge in the frequency domain limits the further improvement of model performance. In this paper, we propose DDLNet, a RSCD network based on dual-domain learning (i.e., frequency and spatial domains). In particular, we design a Frequency-domain Enhancement Module (FEM) to capture frequency components from the input bi-temporal images using Discrete Cosine Transform (DCT) and thus enhance the changes of interest. Besides, we devise a Spatial-domain Recovery Module (SRM) to fuse spatiotemporal features for reconstructing spatial details of change representations. Extensive experiments on three benchmark RSCD datasets demonstrate that the proposed method achieves state-of-the-art performance and reaches a more satisfactory accuracy-efficiency trade-off. Our code is publicly available at https://github.com/xwmaxwma/rschange.
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 | WHU-CD | DDLNet | F1 | 90.56 | #19 of 22 | Archive leaderboard | report |
| Change Detection | WHU-CD | DDLNet | IoU | 82.75 | #19 of 22 | Archive leaderboard | report |
| Change Detection | WHU-CD | DDLNet | Overall Accuracy | 99.13 | #19 of 22 | Archive leaderboard | report |
| Change Detection | WHU-CD | DDLNet | Precision | 91.56 | #19 of 22 | Archive leaderboard | report |
| Change Detection | WHU-CD | DDLNet | Recall | 90.03 | #19 of 22 | 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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