Cloud removal in optical remote sensing imagery is essential for many Earth observation applications. To recover the cloud obscured information, some preconditions must be satisfied. For example, the cloud must be semitransparent or relationships between contaminated and cloud-free pixels must be assumed. Due to the inherent imaging geometry features in satellite remote sensing, it is impossible to observe the ground under the clouds directly; therefore, cloud removal algorithms are always not perfect owing to the loss of ground truth. Recently, the use of passenger aircraft as a platform for remote sensing has been proposed by some researchers and institutes, including Airbus and the Japan Aerospace Exploration Agency. Passenger aircraft have the advantages of short visitation frequency and low cost. Additionally, because passenger aircraft fly at lower altitudes compared to satellites, they can observe the ground under the clouds at an oblique viewing angle. In this study, we examine the possibility of creating cloud-free remote sensing data by stacking multiangle images captured by passenger aircraft. To accomplish this, a processing framework is proposed, which includes four main steps: first, multiangle image acquisition from passenger aircraft, second, cloud detection based on deep learning semantic segmentation models, third, cloud removal by image stacking, and fourth, image quality enhancement via haze removal. This method is intended to remove cloud contamination without the requirements of reference images and predetermination of cloud types. The proposed method was tested in multiple case studies, wherein the resultant cloud- and haze-free orthophotos were visualized and quantitatively analyzed in various land cover type scenes. The results of the case studies demonstrated that the proposed method could generate high quality, cloud-free orthophotos. Therefore, we conclude that this framework has great potential for creating cloud-free remote sensing images when the cloud removal of satellite imagery is difficult or inaccurate.

中文翻译：

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以客机为平台创建无云遥感数据的框架


光学遥感图像中的云去除对于许多地球观测应用至关重要。要恢复云模糊信息，必须满足一些前提条件。例如，云必须是半透明的，或者必须假设受污染像素与无云像素之间的关系。由于卫星遥感固有的成像几何特征，无法直接观测云层下的地面；因此，由于丢失了真实数据，云去除算法总是不完美。最近，包括空中客车公司和日本宇宙航空研究开发机构在内的一些研究人员和机构提出使用客机作为遥感平台。客机具有访问频率短、成本低的优点。此外，由于客机的飞行高度比卫星低，因此它们可以以倾斜的视角观察云层下的地面。在这项研究中，我们研究了通过堆叠客机捕获的多角度图像来创建无云遥感数据的可能性。为此，提出了一个处理框架，包括四个主要步骤：第一，从客机获取多角度图像；第二，基于深度学习语义分割模型的云检测；第三，通过图像叠加去除云；第四，图像质量通过除雾增强。该方法旨在去除云污染，无需参考图像和预先确定云类型。所提出的方法在多个案例研究中进行了测试，其中在各种土地覆盖类型场景中对所得的无云和无霾正射影像进行可视化和定量分析。 案例研究的结果表明，所提出的方法可以生成高质量、无云的正射影像。因此，我们得出的结论是，当卫星图像去云困难或不准确时，该框架在创建无云遥感图像方面具有巨大潜力。