发表机构
Beijing Foreign Studies University（北京外国语大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对遥感变化检测中像素级标注成本高的问题，提出利用变化描述作为唯一监督，通过生成流水线和语义-外观一致性框架（SAAF）实现变化检测，在Flair-RSGen和WHU-CDC数据集上优于有限监督基线。
AI 中文摘要
遥感变化检测（RSCD）对于监测土地覆盖变化和城市发展至关重要。然而，大多数方法需要像素级的变化掩膜，这类标注成本高昂且耗时。弱监督方法通过使用图像级的变化标签来降低这一成本，但这些标签仅指示是否发生变化，模型需要通过额外且复杂的机制来恢复变化的位置和语义信息。这些缺失的信息可以直接由变化描述提供，变化描述能够说明变化的内容、变化后的状态以及发生的位置。因此，我们引入了变化描述引导的RSCD，将变化描述作为唯一的任务特定监督，无需人工标注的变化掩膜即可学习变化掩膜。我们的框架包含两个组件：一个由描述驱动的生成流水线，用于大规模生成与描述匹配且受控变化的双时相遥感图像对；以及一个由描述的转变语义引导的变化检测器。该检测器采用我们的语义-外观一致性框架（SAAF），将基于描述的语义响应与RGB差异相结合以进行变化定位，同时文本条件引导密集预测。在我们新构建的Flair-RSGen数据集和WHU-CDC上的实验表明，在评估协议下，SAAF在宏平均IoU和F1指标上优于最接近的可复现的有限监督基线。代码可在 https://github.com/qianyuancs/SAAF 公开获取。
英文摘要
Remote sensing change detection (RSCD) is essential for monitoring land-cover changes and urban development. However, most methods demand pixel-level change masks, which are costly and time-consuming to annotate. Weakly supervised methods reduce this cost by using image-level change labels. Yet these labels indicate only whether a change occurs, leaving models to recover the location of the change and semantic meaning through additional and complex mechanisms. This missing information can be supplied directly by change captions, which describe what changes, what it becomes, and where it occurs. Therefore, we introduce change-caption-guided RSCD, using change captions as the sole task-specific supervision to learn change masks without manually annotated change masks. Our framework has two components: a caption-driven generation pipeline that produces bi-temporal remote sensing image pairs at scale with controlled changes matching each caption, and a change detector guided by the caption's transition semantics. The detector uses our Semantic-Appearance Agreement Framework (SAAF) to combine caption-grounded semantic responses with RGB differences for change localization, while text conditioning guides dense prediction. Experiments on our newly constructed Flair-RSGen dataset and WHU-CDC show that SAAF outperforms the closest reproduced limited-supervision baselines in macro-averaged IoU and F1 under the evaluated protocols. Code is publicly available at https://github.com/qianyuancs/SAAF.
Comments12 pages, 6 figures, 6 tables. Code: https://github.com/qianyuancs/SAAF
