用图文提示统一遥感变化检测，实现跨场景自适应
OmniCD: A Foundational Framework for Remote Sensing Image Change Detection Guided by Multimodal Semantics
- 融合图像与文本提示构建统一检测框架
- 在30万+图文对数据上达到领先性能
- 适合需要零样本泛化的遥感分析场景
遥感变化检测对城市监测和灾情评估至关重要，但传统方法在不同场景间泛化能力差。我们提出OmniCD，一个通过多模态语义引导统一增强遥感变化检测的基础框架。OmniCD将图像与文本提示（如文字描述、语义图、地理空间元数据）整合到统一架构中，支持从二值变化检测到零样本语义变化理解的任务。框架包含分层场景检索模块与变化检测模块，并引入风格解耦机制提升跨域鲁棒性。我们进一步构建了大规模多模态数据集RSITCD，包含30万+标注的图像-文本对。大量实验证明，OmniCD在多个基准上均达到最先进水平，展现出强适应性，为通用遥感变化检测系统奠定坚实基础。
原文摘要 · Abstract (English)
Change detection (CD) in remote sensing is vital for applications such as urban monitoring and disaster assessment, yet traditional methods struggle with generalization across diverse scenarios. We present OmniCD, a foundational framework that unifies and enhances remote sensing CD through multimodal semantic guidance. OmniCD incorporates image and text prompts -- such as textual descriptions, semantic maps, and geospatial metadata -- into a unified architecture, supporting tasks from binary CD to zero-shot semantic change understanding. The framework integrates a hierarchical scene retrieval module and a change detection module, reinforced by a style disentanglement mechanism for improved cross-domain robustness. We further introduce RSITCD, a large-scale multimodal dataset with 300K+ annotated image-text pairs. Extensive experiments show that OmniCD achieves state-of-the-art performance across benchmarks, demonstrating strong adaptability and setting a solid foundation for general-purpose CD systems in remote sensing.
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