针对遥感影像变化检测中的多源异质数据难题，提出频率感知融合框架。
FAF-CD: Frequency-Aware Fusion for Change Detection under Imperfect Multimodal Remote Sensing
- 采用频域感知的三分支融合机制，结合傅里叶与小波对比增强对齐
- 在多个数据集上实现超过0.92的分类F1值，且在扰动条件下表现最优
- 模型轻量级设计，计算量减少约24 GFLOPs，适合实际部署
真实场景下的遥感变化检测常面临异源观测不完美问题，如事件前后图像存在时间错位、传感器差异、光照、季节及模态偏移。尤其在光学-雷达（EO-SAR）灾情制图中，干扰变化可能被误判为结构损伤。本文提出FAF-CD，一种基于DINOv3预训练的ConvNeXt编码器与线性复杂度VMamba解码器的混合框架。其校正感知三分支融合模块结合可变形空间对齐与傅里叶/哈尔小波比较，通过自适应门控聚合跨尺度互补信息。在BRIGHT验证集上，该方法在匹配的异源EO-SAR设置下，清洁与扰动条件下的tc-mIoU/tc-mAP均优于NeXt2Former-CD。FAF-CD还推广至二值光学变化检测，在LEVIR-CD上取得0.924的cF1，WHU-CD上达0.955，并在伪变化对齐压力测试中于两类数据集上均获得最佳平均扰动cIoU/cF1。同时相比NeXt2Former-CD降低约24 GFLOPs计算开销，精度保持或提升。
原文摘要 · Abstract (English)
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.
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