ChangeFlow——用于遥感变化检测的潜在修正流

摘要

遥感变化检测（RSCD）旨在定位同一地理区域两幅图像之间的变化。实际应用中，变化掩膜通常遵循区域级注释约定而非纯粹的局部外观差异，这使得其具有上下文依赖性且偶尔存在歧义。当前主流方法多采用逐像素判别式分类，这种策略为每个输入生成单一预测，无法将变化区域显式建模为连贯的整体。生成式公式作为一种自然替代方案，能够对合理掩膜的分布进行建模，通过采样捕捉歧义性并促进全局一致性。然而，现有生成式RSCD方法因像素空间生成的高计算成本及条件机制的复杂性，其性能通常落后于强判别式基线。为克服判别式与生成式方法的各自局限，我们提出ChangeFlow——一种生成式框架，通过修正流将变化检测重新表述为潜在空间中变化掩膜的合成过程。ChangeFlow由结构化且轻量级的条件信号引导，其随机设计天然支持基于采样的预测集成。具体而言，聚合多个预测变化掩膜可提升鲁棒性，而样本一致性则提供实用的置信度估计，突出显示歧义区域。在四个基准数据集上，ChangeFlow的平均F1达到80.4%，相比此前最优方法平均提升1.3个百分点，同时推理速度与近期强基线方法相当。项目页面：https://blaz-r.github.io/changeflow_cd

English

Remote sensing change detection (RSCD) aims to localise changes between two images of the same geographic region. In practice, change masks often follow region-level annotation conventions rather than purely local appearance differences, making them context-dependent and occasionally ambiguous. Most state-of-the-art methods utilise per-pixel discriminative classification, which produces a single prediction per input and fails to explicitly model the changed region as a coherent whole. A natural alternative is generative formulation, which can model a distribution of plausible masks, enabling sampling to capture ambiguity and encourage global consistency. However, existing generative RSCD approaches typically lag behind strong discriminative baselines due to the high computational cost of pixel-space generation and the complexity of their conditioning mechanisms. To address the limitations of prior discriminative and generative methods, we propose ChangeFlow, a generative framework that reformulates change detection as the synthesis of a change mask in latent space via rectified flow. ChangeFlow is guided by a structured yet lightweight conditioning signal, and its stochastic design naturally supports sampling-based prediction ensembling. Namely, aggregating multiple predicted change masks improves robustness, while sample agreement provides a practical confidence estimation that highlights ambiguous regions. Across four benchmarks, ChangeFlow achieves an average F1 of 80.4\%, improving by 1.3 points on average over the previous best method, while maintaining inference speed comparable to recent strong baselines. Project page: https://blaz-r.github.io/changeflow_cd