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

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Abstract

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.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (61975222).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Infrared Perception, Chinese Academy of Sciences, Shanghai, 200083, China

    Liyuan Li, Xiaoyan Li, Linyi Jiang, Xiaofeng Su & Fansheng Chen

  2. Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou, 310024, China

    Xiaoyan Li

  3. Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai, 200083, China

    Liyuan Li, Xiaoyan Li, Linyi Jiang, Xiaofeng Su & Fansheng Chen

  4. University of Chinese Academy of Sciences, Beijing, 100049, China

    Liyuan Li, Xiaoyan Li, Linyi Jiang & Fansheng Chen

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  1. Liyuan Li
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Li, L., Li, X., Jiang, L. et al. A review on deep learning techniques for cloud detection methodologies and challenges. SIViP 15, 1527–1535 (2021). https://doi.org/10.1007/s11760-021-01885-7

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