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| Main Authors: | , |
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| Format: | Preprint |
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2025
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| Online Access: | https://arxiv.org/abs/2509.15563 |
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| _version_ | 1866912594512576512 |
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| author | Sun, Min Guo, Fenghui |
| author_facet | Sun, Min Guo, Fenghui |
| contents | Remote sensing change detection (RSCD) is vital for identifying land-cover changes, yet existing methods, including state-of-the-art State Space Models (SSMs), often lack explicit mechanisms to handle geometric misalignments and struggle to distinguish subtle, true changes from noise.To address this, we introduce DC-Mamba, an "align-then-enhance" framework built upon the ChangeMamba backbone. It integrates two lightweight, plug-and-play modules: (1) Bi-Temporal Deformable Alignment (BTDA), which explicitly introduces geometric awareness to correct spatial misalignments at the semantic feature level; and (2) a Scale-Sparse Change Amplifier(SSCA), which uses multi-source cues to selectively amplify high-confidence change signals while suppressing noise before the final classification. This synergistic design first establishes geometric consistency with BTDA to reduce pseudo-changes, then leverages SSCA to sharpen boundaries and enhance the visibility of small or subtle targets. Experiments show our method significantly improves performance over the strong ChangeMamba baseline, increasing the F1-score from 0.5730 to 0.5903 and IoU from 0.4015 to 0.4187. The results confirm the effectiveness of our "align-then-enhance" strategy, offering a robust and easily deployable solution that transparently addresses both geometric and feature-level challenges in RSCD. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_15563 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DC-Mamba: Bi-temporal deformable alignment and scale-sparse enhancement for remote sensing change detection Sun, Min Guo, Fenghui Computer Vision and Pattern Recognition Remote sensing change detection (RSCD) is vital for identifying land-cover changes, yet existing methods, including state-of-the-art State Space Models (SSMs), often lack explicit mechanisms to handle geometric misalignments and struggle to distinguish subtle, true changes from noise.To address this, we introduce DC-Mamba, an "align-then-enhance" framework built upon the ChangeMamba backbone. It integrates two lightweight, plug-and-play modules: (1) Bi-Temporal Deformable Alignment (BTDA), which explicitly introduces geometric awareness to correct spatial misalignments at the semantic feature level; and (2) a Scale-Sparse Change Amplifier(SSCA), which uses multi-source cues to selectively amplify high-confidence change signals while suppressing noise before the final classification. This synergistic design first establishes geometric consistency with BTDA to reduce pseudo-changes, then leverages SSCA to sharpen boundaries and enhance the visibility of small or subtle targets. Experiments show our method significantly improves performance over the strong ChangeMamba baseline, increasing the F1-score from 0.5730 to 0.5903 and IoU from 0.4015 to 0.4187. The results confirm the effectiveness of our "align-then-enhance" strategy, offering a robust and easily deployable solution that transparently addresses both geometric and feature-level challenges in RSCD. |
| title | DC-Mamba: Bi-temporal deformable alignment and scale-sparse enhancement for remote sensing change detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.15563 |