将开放词汇语义分割模型适配到遥感变化检测，实现任意类别变化识别。
Seg2Change: Adapting Open-Vocabulary Semantic Segmentation Model for Remote Sensing Change Detection
- 设计无类别依赖的变化检测头，支持任意类别变化识别。
- 在WHU-CD和SECOND数据集上分别提升9.52和5.50的IoU/mIoU。
- 开源框架，无需复杂改造即可复用先进分割模型。
变化检测是遥感中的基础任务，用于量化人类活动与生态动态对地表覆盖的影响。现有方法受限于训练数据中预定义类别，难以适应真实场景。近年来，大量面向遥感影像的开放词汇语义分割模型涌现，但缺乏有效框架将其直接应用于开放词汇变化检测（OVCD）——这一融合视觉与语言、可检测任意类别变化的新任务。为此，我们构建了一个无类别依赖的变化检测数据集CA-CDD，设计了无类别依赖的变化检测头，以识别任意类别的变化并映射至具体类别。基于此，提出Seg2Change适配器，将开放词汇语义分割模型迁移到变化检测任务。无需复杂结构，该简单高效框架在WHU-CD上实现+9.52 IoU，在SECOND上实现+5.50 mIoU，达到当前最优性能。代码已开源：https://github.com/yogurts-sy/Seg2Change。
原文摘要 · Abstract (English)
Change detection is a fundamental task in remote sensing, aiming to quantify the impacts of human activities and ecological dynamics on land-cover changes. Existing change detection methods are limited to predefined classes in training datasets, which constrains their scalability in real-world scenarios. In recent years, numerous advanced open-vocabulary semantic segmentation models have emerged for remote sensing imagery. However, there is still a lack of an effective framework for directly applying these models to open-vocabulary change detection (OVCD), a novel task that integrates vision and language to detect changes across arbitrary categories. To address these challenges, we first construct a category-agnostic change detection dataset, termed CA-CDD. Further, we design a category-agnostic change head to detect the transitions of arbitrary categories and index them to specific classes. Based on them, we propose Seg2Change, an adapter designed to adapt open-vocabulary semantic segmentation models to change detection task. Without bells and whistles, this simple yet effective framework achieves state-of-the-art OVCD performance (+9.52 IoU on WHU-CD and +5.50 mIoU on SECOND). Our code is released at https://github.com/yogurts-sy/Seg2Change.
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