Papers › Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep...
Building Change Detection for Remote Sensing Images Using a Dual Task Constrained Deep Siamese Convolutional Network Model
Yi Liu, Chao Pang, Zongqian Zhan, Xiaomeng Zhang, Xue Yang
In recent years, building change detection methods have made great progress by introducing deep learning, but they still suffer from the problem of the extracted features not being discriminative enough, resulting in incomplete regions and irregular boundaries. To tackle this problem, we propose a dual task constrained deep Siamese convolutional network (DTCDSCN) model, which contains three sub-networks: a change detection network and two semantic segmentation networks. DTCDSCN can accomplish both change detection and semantic segmentation at the same time, which can help to learn more discriminative object-level features and obtain a complete change detection map. Furthermore, we introduce a dual attention module (DAM) to exploit the interdependencies between channels and spatial positions, which improves the feature representation. We also improve the focal loss function to suppress the sample imbalance problem. The experimental results obtained with the WHU building dataset show that the proposed method is effective for building change detection and achieves a state-of-the-art performance in terms of four metrics: precision, recall, F1-score, and intersection over union.
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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 | DTCDSCN | F1 | 89.75 | #20 of 22 | Archive leaderboard | report |
| Change Detection | WHU-CD | DTCDSCN | IoU | 81.40 | #20 of 22 | Archive leaderboard | report |
| Change Detection | WHU-CD | DTCDSCN | Precision | 90.15 | #20 of 22 | Archive leaderboard | report |
| Change Detection | WHU-CD | DTCDSCN | Recall | 89.35 | #20 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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