AI RESEARCH
PhyUnfold-Net: Advancing Remote Sensing Change Detection with Physics-Guided Deep Unfolding
arXiv CS.CV
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ArXi:2603.19566v1 Announce Type: new 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.