PhyUnfold-Net: Advancing Remote Sensing Change Detection with Physics-Guided Deep Unfolding
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| Format: | Preprint |
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2026
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| _version_ | 1866912979925073920 |
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| author | Lei, Zelin Ren, Yaoxing Chang, Jiaming |
| author_facet | Lei, Zelin Ren, Yaoxing Chang, Jiaming |
| contents | Bi-temporal change detection is highly sensitive to acquisition discrepancies, including illumination, season, and atmosphere, which often cause false alarms. We observe that genuine changes exhibit higher patch-wise singular-value entropy (SVE) than pseudo changes in the feature-difference space. Motivated by this physical prior, we propose PhyUnfold-Net, a physics-guided deep unfolding framework that formulates change detection as an explicit decomposition problem. The proposed Iterative Change Decomposition Module (ICDM) unrolls a multi-step solver to progressively separate mixed discrepancy features into a change component and a nuisance component. To stabilize this process, we introduce a staged Exploration-and-Constraint loss (S-SEC), which encourages component separation in early steps while constraining nuisance magnitude in later steps to avoid degenerate solutions. We further design a Wavelet Spectral Suppression Module (WSSM) to suppress acquisition-induced spectral mismatch before decomposition. Experiments on four benchmarks show improvements over state-of-the-art methods, with gains under challenging conditions. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_19566 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | PhyUnfold-Net: Advancing Remote Sensing Change Detection with Physics-Guided Deep Unfolding Lei, Zelin Ren, Yaoxing Chang, Jiaming Computer Vision and Pattern Recognition Bi-temporal change detection is highly sensitive to acquisition discrepancies, including illumination, season, and atmosphere, which often cause false alarms. We observe that genuine changes exhibit higher patch-wise singular-value entropy (SVE) than pseudo changes in the feature-difference space. Motivated by this physical prior, we propose PhyUnfold-Net, a physics-guided deep unfolding framework that formulates change detection as an explicit decomposition problem. The proposed Iterative Change Decomposition Module (ICDM) unrolls a multi-step solver to progressively separate mixed discrepancy features into a change component and a nuisance component. To stabilize this process, we introduce a staged Exploration-and-Constraint loss (S-SEC), which encourages component separation in early steps while constraining nuisance magnitude in later steps to avoid degenerate solutions. We further design a Wavelet Spectral Suppression Module (WSSM) to suppress acquisition-induced spectral mismatch before decomposition. Experiments on four benchmarks show improvements over state-of-the-art methods, with gains under challenging conditions. |
| title | PhyUnfold-Net: Advancing Remote Sensing Change Detection with Physics-Guided Deep Unfolding |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.19566 |