论文标题
多传感器数据融合，用于全球和全季节苏故事图像中的云去除
Multi-Sensor Data Fusion for Cloud Removal in Global and All-Season Sentinel-2 Imagery
论文作者
论文摘要
这项工作已被IEEE TGRS接受出版。通过太空传播地球图像获得的大多数光学观察结果都受云的影响。尽管对重建云覆盖的信息进行了许多先前的工作，但以前的研究通常仅限于狭义的感兴趣的区域，这提出了一个问题，即一种方法是否可以推广到在可变云覆盖范围或不同地区或不同地区和季节中获得的各种观察结果。我们通过策划一个大型新型数据集来训练新的云清除方法，并对最近提出的图像质量和多样性的两个绩效指标进行评估，以针对概括的挑战。我们的数据集是第一个公开包含共同注册的雷达和光学观察结果的全局样本，云和无云的样本。基于云覆盖范围在晴朗的天空和绝对覆盖范围之间有很大变化的观察结果，我们提出了一个新型模型，该模型可以处理极端并评估其在我们提出的数据集上的性能。最后，我们证明了训练模型对实际综合数据的优越性，从而强调了对经过精心策划的真实观测数据集的需求。为了促进未来的研究，我们的数据集可在线提供
This work has been accepted by IEEE TGRS for publication. The majority of optical observations acquired via spaceborne earth imagery are affected by clouds. While there is numerous prior work on reconstructing cloud-covered information, previous studies are oftentimes confined to narrowly-defined regions of interest, raising the question of whether an approach can generalize to a diverse set of observations acquired at variable cloud coverage or in different regions and seasons. We target the challenge of generalization by curating a large novel data set for training new cloud removal approaches and evaluate on two recently proposed performance metrics of image quality and diversity. Our data set is the first publically available to contain a global sample of co-registered radar and optical observations, cloudy as well as cloud-free. Based on the observation that cloud coverage varies widely between clear skies and absolute coverage, we propose a novel model that can deal with either extremes and evaluate its performance on our proposed data set. Finally, we demonstrate the superiority of training models on real over synthetic data, underlining the need for a carefully curated data set of real observations. To facilitate future research, our data set is made available online
