Not All Pixels Flow Alike: Spatial-Clock Flow Matching for Remote Sensing Change Detection

Remote sensing change detection (RSCD) aims to identify genuine scene changes from bitemporal imagery, yet visual discrepancies caused by illumination, blur, noise, and residual misregistration are often entangled with true changes. Existing methods mainly learn discriminative mappings from bitemporal observations to change masks. However, implicitly coupling nuisance suppression and change structure recovery makes it difficult to distinguish appearance variations from genuine changes, especially under weak changes and severe nuisance interference. To explicitly model this correction process, we formulate RSCD as continuous evidence-to-geometry transport using Flow Matching (FM). Nevertheless, applying FM to RSCD requires explicit modeling of nuisance-consistent target coupling and geometry-guided spatial conditioning. We therefore propose TempoFlow-CD, which reformulates FM through target coupling and temporal parameterization. Nuisance-Invariant Geometry Flow (NIGF) aligns nuisance-varied observations with shared mask–distance geometry endpoints, while Change-Adaptive Spatial Clock (CASC) enables content-aware transport progression through adaptive local phases and velocity refinement. Extensive experiments on six RSCD benchmarks demonstrate that TempoFlow-CD achieves superior F1 and IoU performance with improved boundary localization and robustness to radiometric perturbations.