A review on deep learning techniques for cloud detection methodologies and challenges

Cloud detection (CD) with deep learning (DL) algorithms has been greatly developed in the applications involving the predictions of extreme weather and climate. In this review, the different conventional CD methods based on threshold, time differentiation, machine learning, and the intelligent algorithms including convolution neural networks (CNN), simple linear iterative clustering (SLIC), and semantic segmentation algorithms (SSAs) are introduced in detail, and, especially, the majority of CD publications employing the advanced and prevalent DL algorithms during the last decade are summarized and analyzed. First, in terms of the detection for different types of clouds, we meticulously compare the labels, scenarios and volumes of three popular CD datasets and put forward further the constructive recommendations about the cloud images selection, multi-bands images preprocessing, and truth labels combination for creating similar datasets. Subsequently, the structures, detection accuracies, and operating speeds of several different CD network models comprising the fully convolutional neural networks (FCNs), U-Net, SegNet, pyramid scene parsing network (PSPNet), as well as the associated derivatives are conducted elaborately to explore the comprehensively optimal performance for CD. In addition, aiming at expanding the applications in the resource-limited space-borne environment, we conclude the mainstream compression strategies of a number of different lightweight networks. Finally, the various limitations constraining the performance of the existing state-of-the-art DL CD methods and the corresponding development tendency are presented, which, expectantly, could be referential for the following researches.