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 algor...

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Bibliographic Details
Published in:Signal, image and video processing Vol. 15; no. 7; pp. 1527 - 1535
Main Authors: Li, Liyuan, Li, Xiaoyan, Jiang, Linyi, Su, Xiaofeng, Chen, Fansheng
Format: Journal Article
Language:English
Published: London Springer London 01.10.2021
Springer Nature B.V
Subjects:
Algorithms
Artificial neural networks
Clouds
Clustering
Computer Imaging
Computer Science
Datasets
Deep learning
Image Processing and Computer Vision
Image segmentation
Iterative methods
Labels
Machine learning
Multimedia Information Systems
Neural networks
Original Paper
Pattern Recognition and Graphics
Signal,Image and Speech Processing
Vision
Cloud Detection (CD)
Remote sensing images
Semantic Segmentation Algorithms (SSAs)
Deep Learning (DL)
ISSN:1863-1703, 1863-1711
Online Access:Get full text
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Summary: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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ISSN:1863-1703
1863-1711
DOI:10.1007/s11760-021-01885-7