用生成模型解决遥感图像跨域适配，提升环境监测泛化能力
FlowEO: Generative Unsupervised Domain Adaptation for Earth Observation
- 基于流匹配学习源到目标图像分布的语义保持映射
- 在四组遥感数据上超越现有方法，分类与分割性能更优
- 适合遥感图像分析、灾害监测等实际应用场景
地球观测数据的日益丰富为大规模环境监测提供了前所未有的机遇。然而，这些数据来自不同传感器、地理区域、获取时间及大气条件，存在显著分布差异，严重限制了预训练遥感模型的泛化能力，因此无监督域适应（UDA）对实际应用至关重要。我们提出FlowEO，一种基于生成模型的图像空间无监督域适应新框架。利用流匹配技术学习从源域到目标域的语义保持映射，实现对遥感图像分类与语义分割中复杂域偏移的有效适应。我们在四个数据集上进行了广泛实验，涵盖合成孔径雷达（SAR）转光学影像、自然灾害引发的时间与语义漂移等场景。结果表明，FlowEO在域适应性能上优于现有图像翻译方法，同时保持相当或更优的感知图像质量，凸显了基于流匹配的域适应在遥感领域的潜力。
原文摘要 · Abstract (English)
The increasing availability of Earth observation data offers unprecedented opportunities for large-scale environmental monitoring and analysis. However, these datasets are inherently heterogeneous, stemming from diverse sensors, geographical regions, acquisition times, and atmospheric conditions. Distribution shifts between training and deployment domains severely limit the generalization of pretrained remote sensing models, making unsupervised domain adaptation (UDA) crucial for real-world applications. We introduce FlowEO, a novel framework that leverages generative models for image-space UDA in Earth observation. We leverage flow matching to learn a semantically preserving mapping that transports from the source to the target image distribution. This allows us to tackle challenging domain adaptation configurations for classification and semantic segmentation of Earth observation images. We conduct extensive experiments across four datasets covering adaptation scenarios such as SAR to optical translation and temporal and semantic shifts caused by natural disasters. Experimental results demonstrate that FlowEO outperforms existing image translation approaches for domain adaptation while achieving on-par or better perceptual image quality, highlighting the potential of flow-matching-based UDA for remote sensing.
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