RIFT-CD: Preserving Spatial Detail and Relational Invariance for Robust Remote Sensing Change Detection

Remote sensing change detection requires simultaneously preserving fine-grained spatial evidence and learning temporal representations that generalize across diverse scenes. However, modern pretrained vision backbones often compress spatial information aggressively, while conventional temporal fusion can still exploit scenespecific appearance cues, limiting robustness under small structural changes and distribution shifts. We introduce RIFT-CD, a relationally normalized, resolutionpreserving backbone for change detection. Rather than treating temporal reasoning as an auxiliary prediction component, RIFT-CD integrates change-sensitive representation learning directly into the feature trunk. The architecture maintains high-resolution semantic representations throughout the hierarchy, adopts information-preserving feature transitions, and performs temporal interaction in a content-normalized relational space. We further regularize backbone adaptation to retain transferable pretrained representations while preventing late-stage specialization to training scenes. This design provides a unified way to balance spatial fidelity, temporal discrimination, and cross-scene generalization within a single end-to-end model. Experiments across multiple change-detection benchmarks demonstrate consistent improvements in segmentation quality, boundary localization, and robustness to appearance variation, suggesting that preserving information and constraining representation drift are central to building more generalizable change detectors.