Papers › A Transformer-Based Siamese Network for Change Detection
A Transformer-Based Siamese Network for Change Detection
Wele Gedara Chaminda Bandara, Vishal M. Patel
This paper presents a transformer-based Siamese network architecture (abbreviated by ChangeFormer) for Change Detection (CD) from a pair of co-registered remote sensing images. Different from recent CD frameworks, which are based on fully convolutional networks (ConvNets), the proposed method unifies hierarchically structured transformer encoder with Multi-Layer Perception (MLP) decoder in a Siamese network architecture to efficiently render multi-scale long-range details required for accurate CD. Experiments on two CD datasets show that the proposed end-to-end trainable ChangeFormer architecture achieves better CD performance than previous counterparts. Our code is available at https://github.com/wgcban/ChangeFormer.
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Code
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Code Syntology ran Syntology
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Building change detection for remote sensing images | LEVIR-CD | ChangeFormer | F1 | 90.40 | #26 of 37 | Archive leaderboard | report |
| Building change detection for remote sensing images | LEVIR-CD | ChangeFormer | IoU | 82.48 | #26 of 37 | Archive leaderboard | report |
| Change Detection | LEVIR-CD | ChangeFormer | F1 | 90.4 | #24 of 28 | Archive leaderboard | report |
| Change Detection | LEVIR-CD | ChangeFormer | IoU | 82.48 | #24 of 28 | Archive leaderboard | report |
| Change Detection | LEVIR-CD | ChangeFormer | Overall Accuracy | 99.04 | #24 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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