用物理先验分离遥感影像变化与干扰，提升检测准确性
PhyUnfold-Net: Advancing Remote Sensing Change Detection with Physics-Guided Deep Unfolding
- 基于特征差空间的奇异值熵差异，设计可解释的迭代分解模块
- 在四个基准上显著降低虚假报警率，尤其在光照季节变化下表现优
- 适合需要高鲁棒性的遥感变化检测场景，如环境监测
双时相变化检测易受光照、季节、大气等采集差异影响，导致误报。我们发现真实变化在特征差空间中具有更高的局部奇异值熵（SVE），而伪变化则较低。受此物理规律启发，提出物理引导的深度展开框架PhyUnfold-Net，将变化检测建模为显式分解问题。提出的迭代变化分解模块（ICDM）展开多步求解器，逐步分离混合的差异特征为变化成分与干扰成分。为稳定过程，引入分阶段探索与约束损失（S-SEC），早期鼓励成分分离，后期约束干扰幅度以避免退化解。此外设计小波谱抑制模块（WSSM），在分解前抑制采集引起的光谱不匹配。在四个基准上的实验表明，该方法优于现有最优模型，尤其在挑战性条件下表现更佳。
原文摘要 · Abstract (English)
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.
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