PhyUnfold-Net: Advancing Remote Sensing Change Detection with Physics-Guided Deep Unfolding
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 introd...
| Authors | Zelin Lei, Yaoxing Ren, Jiaming Chang |
|---|---|
| Primary category | cs.CV |
| Published | 20 March 2026 |
| Last updated at source | 31 July 2026 |
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