FAF-CD: Frequency-Aware Fusion for Change Detection under Imperfect Multimodal Remote Sensing
For remote sensing change detection practitioners, this work addresses the challenging problem of nuisance variation in heterogeneous observations, though the gains are incremental over existing methods.
FAF-CD proposes a frequency-aware hybrid framework for change detection under imperfect multimodal remote sensing (e.g., EO-SAR), achieving up to 0.924 cF1 on LEVIR-CD and 0.955 cF1 on WHU-CD, with a 24 GFLOPs reduction over NeXt2Former-CD while maintaining or improving accuracy.
Remote sensing change detection for real-world monitoring often relies on imperfect heterogeneous observations, where pre- and post-event images may be asynchronous, cross-sensor, or affected by illumination, seasonal, and modality shifts. This setting is especially challenging for EO-SAR disaster mapping, where nuisance variation can resemble structural damage. We propose FAF-CD, a frequency-aware hybrid framework with a DINOv3-pretrained ConvNeXt encoder and a linear-complexity VMamba-based decoder. Its rectification-aware tri-branch fusion module combines deformable spatial alignment with Fourier and Haar-wavelet comparisons, using adaptive gating to aggregate complementary cues across scales. On BRIGHT validation, a matched heterogeneous EO-SAR adaptation improves clean and perturbed tc-mIoU/tc-mAP over NeXt2Former-CD. FAF-CD also generalizes to binary optical CD, achieving 0.924 cF1 on LEVIR-CD and 0.955 cF1 on WHU-CD, and obtains the best average perturbed cIoU/cF1 on both binary datasets among M-CD and NeXt2Former-CD under pseudo-change-aligned stress tests. It further reduces cost by approximately 24 GFLOPs relative to NeXt2Former-CD while maintaining or improving accuracy.