What Caused Changes? Causal Physics Disentanglement Network for Remote Sensing Change Detection

To address sensitivity to imaging environment variation and limited physical interpretability in traditional remote sensing change detection (RSCD), we propose causal physics disentanglement network combining RS imaging physics with causal inference. Physical parameter decomposition separates intrinsic ground-object information from environmental interference. Moreover, surface reflectance difference serves as core causal evidence for change discrimination, while non-causal backdoor paths induced by illumination and atmospheric variation are explicitly blocked. Based on main RS energy transfer paths (i.e., direct solar irradiance, diffuse sky irradiance, and atmospheric path radiance), spectral radiance model of ground objects is firstly constructed.Secondly, causal inference clarifies relationships between physical variables and RSCD. Specifically, surface reflectance variation serves as core physical evidence of real ground-object change. Meanwhile, total irradiance and atmospheric transmittance are treated as non-causal imaging factors that may induce pseudo-change response. Building on this, bi-temporal physical parameter decomposition and reconstruction network is developed to disentangle input image features into total irradiance, surface reflectance, and atmospheric transmittance, etc. Moreover, orthogonality, reconstruction, and spatial smoothness constraints are jointly imposed to ensure decomposed physical parameters plausibility. Finally, reflectance guided dual-level consistency constraint is introduced. Bi-temporal reflectance difference serves as primary causal pathway for RSCD. Specifically, it guides semantic branch at feature and prediction levels toward real ground-object change. Meanwhile, non-causal backdoor paths from total irradiance and atmospheric transmittance through observed imagery are explicitly blocked. Experimental results demonstrate that proposed model outperforms existing state-of-the-art methods in both qualitative and quantitative metrics on multiple public RSCD datasets, fully verifying its effectiveness and adaptability to complex scenes.