Optical remote sensing imagery is at the core of many Earth observation activities. The regular, consistent and global-scale nature of the satellite data is exploited in many applications, such as cropland monitoring, climate change assessment, land-cover and land-use classification, and disaster assessment. However, one main problem severely affects the temporal and spatial availability of surface observations, namely cloud cover. The task of removing clouds from optical images has been subject of studies since decades. The advent of the Big Data era in satellite remote sensing opens new possibilities for tackling the problem using powerful data-driven deep learning methods.

In this paper, a deep residual neural network architecture is designed to remove clouds from multispectral Sentinel-2 imagery. SAR-optical data fusion is used to exploit the synergistic properties of the two imaging systems to guide the image reconstruction. Additionally, a novel cloud-adaptive loss is proposed to maximize the retainment of original information. The network is trained and tested on a globally sampled dataset comprising real cloudy and cloud-free images. The proposed setup allows to remove even optically thick clouds by reconstructing an optical representation of the underlying land surface structure.

光学遥感影像是许多地球观测活动的核心。卫星数据的定期，一致和全球规模的性质在许多应用中得到了利用，例如农田监测，气候变化评估，土地覆盖和土地利用分类以及灾害评估。但是，一个主要问题严重影响了地面观测的时间和空间可用性，即云层覆盖。从光学图像中去除云的任务几十年来一直是研究的主题。卫星遥感大数据时代的到来为使用强大的数据驱动的深度学习方法解决该问题提供了新的可能性。

本文设计了一种深度残差神经网络架构，以从多光谱Sentinel-2影像中去除云。SAR光学数据融合用于开发两个成像系统的协同特性，以指导图像重建。另外，提出了一种新颖的云自适应损失来最大程度地保留原始信息。该网络在包含真实阴天和无云图像的全球采样数据集上进行了培训和测试。所提出的设置允许通过重建下面的陆地表面结构的光学表示来去除甚至光学上较厚的云。