用生成模型融合卫星数据，实现高频无云高分辨率影像合成
SatFlow: Generative model based framework for producing High Resolution Gap Free Remote Sensing Imagery
- 基于条件流匹配训练生成模型，融合MODIS与Landsat数据
- 在云覆盖区域和扫描线缺口处重建影像，保持结构与光谱一致性
- 适合农业监测、环境变化检测等需要连续高清影像的应用
频繁的高分辨率遥感影像对农业与环境监测至关重要。Landsat系列卫星提供30米分辨率影像，但时间频率较低；而MODIS和VIIRS等任务虽实现每日覆盖，但空间分辨率较粗。光学遥感观测中约55%受云及云影污染。为此，本文提出SatFlow——一种基于生成模型的框架，融合低分辨率的MODIS影像与Landsat观测，生成高频、高分辨率且无缺失的表面反射率影像。模型采用条件流匹配进行训练，在保留结构与光谱完整性方面表现更优。云掩膜处理被建模为图像修复任务，通过学习到的生成过程在推理时重构被云遮挡像素，并填补扫描线造成的空缺。实验表明该方法能可靠地恢复云覆盖区域。这一能力对作物物候追踪、环境变化检测等下游应用极为关键。
原文摘要 · Abstract (English)
Frequent, high-resolution remote sensing imagery is crucial for agricultural and environmental monitoring. Satellites from the Landsat collection offer detailed imagery at 30m resolution but with lower temporal frequency, whereas missions like MODIS and VIIRS provide daily coverage at coarser resolutions. Clouds and cloud shadows contaminate about 55\% of the optical remote sensing observations, posing additional challenges. To address these challenges, we present SatFlow, a generative model-based framework that fuses low-resolution MODIS imagery and Landsat observations to produce frequent, high-resolution, gap-free surface reflectance imagery. Our model, trained via Conditional Flow Matching, demonstrates better performance in generating imagery with preserved structural and spectral integrity. Cloud imputation is treated as an image inpainting task, where the model reconstructs cloud-contaminated pixels and fills gaps caused by scan lines during inference by leveraging the learned generative processes. Experimental results demonstrate the capability of our approach in reliably imputing cloud-covered regions. This capability is crucial for downstream applications such as crop phenology tracking, environmental change detection etc.,
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