Abstract
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.,
Abstract (translated)
频繁的高分辨率遥感图像对于农业和环境监测至关重要。Landsat卫星系列提供的30米分辨率影像虽然详细，但时间频率较低；而像MODIS和VIIRS这样的任务则提供每日覆盖范围，尽管其空间分辨率较粗。大约55%的光学遥感观测被云层和云影污染，这给数据利用带来了额外挑战。为了解决这些问题，我们提出了SatFlow框架，这是一个基于生成模型的方法，它融合了低分辨率的MODIS影像与Landsat观测结果，以生产频繁、高分辨率且无缺口的表面反射率图像。 我们的模型通过条件流匹配进行训练，在生成保持结构和光谱完整性的影像方面表现更佳。云层填补被视为一个图像修复任务，其中模型重建被污染的像素，并在推理过程中利用所学习到的生成过程来填充扫描线造成的空缺区域。实验结果表明了该方法可靠地填补云覆盖区域的能力。这种能力对于下游应用（如作物生长阶段追踪、环境变化检测等）至关重要。
URL
https://arxiv.org/abs/2502.01098
