用DINOv3提升遥感建筑变化检测，更准更鲁棒。
ChangeDINO: DINOv3-Driven Building Change Detection in Optical Remote Sensing Imagery
- 双流结构融合轻量主干与冻结DINOv3特征，增强语义表达
- 多尺度差分变换器利用绝对差异作为变化先验，抑制干扰
- 可学习形态模块优化边界，适合小样本和复杂光照场景
遥感变化检测（RSCD）旨在从配准的双时相影像中识别地表变化。然而，许多基于深度学习的方法仅依赖变化图标注，未充分利用非变化区域的语义信息，导致在光照变化、非正射视角和标签稀缺情况下鲁棒性不足。本文提出ChangeDINO，一种端到端多尺度孪生框架，用于光学遥感影像中的建筑变化检测。模型融合轻量级主干网络与冻结DINOv3提取的特征，即使在小数据集上也能生成富含语义与上下文的特征金字塔。空间-谱差分变压器解码器利用多尺度绝对差异作为变化先验，突出真实建筑变化并抑制无关响应。最后，可学习形态模块对上采样后的逻辑值进行细化，恢复清晰边界。在四个公开基准上的实验表明，ChangeDINO在IoU和F1指标上持续优于近期先进方法。消融实验验证了各组件的有效性。源代码已开源。
原文摘要 · Abstract (English)
Remote sensing change detection (RSCD) aims to identify surface changes from co-registered bi-temporal images. However, many deep learning-based RSCD methods rely solely on change-map annotations and underuse the semantic information in non-changing regions, which limits robustness under illumination variation, off-nadir views, and scarce labels. This article introduces ChangeDINO, an end-to-end multiscale Siamese framework for optical building change detection. The model fuses a lightweight backbone stream with features transferred from a frozen DINOv3, yielding semantic- and context-rich pyramids even on small datasets. A spatial-spectral differential transformer decoder then exploits multi-scale absolute differences as change priors to highlight true building changes and suppress irrelevant responses. Finally, a learnable morphology module refines the upsampled logits to recover clean boundaries. Experiments on four public benchmarks show that ChangeDINO consistently outperforms recent state-of-the-art methods in IoU and F1, and ablation studies confirm the effectiveness of each component. The source code is available at https://github.com/chingheng0808/ChangeDINO.
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