Abstract
Change detection in remote sensing imagery is essential for applications such as urban planning, environmental monitoring, and disaster management. Traditional change detection methods typically identify all changes between two temporal images without distinguishing the types of transitions, which can lead to results that may not align with specific user needs. Although semantic change detection methods have attempted to address this by categorizing changes into predefined classes, these methods rely on rigid class definitions and fixed model architectures, making it difficult to mix datasets with different label sets or reuse models across tasks, as the output channels are tightly coupled with the number and type of semantic classes. To overcome these limitations, we introduce Referring Change Detection (RCD), which leverages natural language prompts to detect specific classes of changes in remote sensing images. By integrating language understanding with visual analysis, our approach allows users to specify the exact type of change they are interested in. However, training models for RCD is challenging due to the limited availability of annotated data and severe class imbalance in existing datasets. To address this, we propose a two-stage framework consisting of (I) \textbf{RCDNet}, a cross-modal fusion network designed for referring change detection, and (II) \textbf{RCDGen}, a diffusion-based synthetic data generation pipeline that produces realistic post-change images and change maps for a specified category using only pre-change image, without relying on semantic segmentation masks and thereby significantly lowering the barrier to scalable data creation. Experiments across multiple datasets show that our framework enables scalable and targeted change detection. Project website is here: this https URL.
Abstract (translated)
在遥感图像中进行变化检测对于城市规划、环境监测和灾害管理等应用至关重要。传统的变化检测方法通常会在两个时间点的影像之间识别所有变化，而不区分这些变化的具体类型，这可能导致的结果可能无法满足特定用户的需求。虽然语义变化检测方法试图通过将变化归类到预定义的类别来解决这一问题，但这些方法依赖于固定的类别定义和固定模型架构，使得难以混合具有不同标签集的数据集或在任务间重复使用模型，因为输出通道紧密耦合了类别数量及类型。 为克服这些问题，我们引入了基于自然语言提示的变化参考检测（RCD），它能够针对遥感图像中特定类别的变化进行识别。通过将语言理解和视觉分析相结合，我们的方法允许用户具体指定他们感兴趣的变化类型。然而，由于标注数据的有限可用性以及现有数据集中类别不平衡的问题，训练用于RCD的模型具有挑战性。 为解决这些问题，我们提出了一种两阶段框架：(I) **RCDNet**，这是一种专门针对变化参考检测设计的跨模态融合网络；(II) **RCDGen**，这是一个基于扩散的合成数据生成流水线，能够仅使用前一时间点的图像来生成特定类别的真实感变化后的影像和变化图，无需依赖语义分割掩码，并且显著降低了可扩展性创建数据的门槛。 在多个数据集上的实验表明，我们的框架能够实现规模化的、有针对性的变化检测。项目的官方网站链接：[这里](this https URL)。
URL
https://arxiv.org/abs/2512.11719
