干旱区生态环境知识资源中心(Arid): Using Convolutional Neural Networks for Cloud Detection on VENμS Images over Multiple Land-Cover Types

Arid

DOI	10.3390/rs14205210
	Using Convolutional Neural Networks for Cloud Detection on VENμS Images over Multiple Land-Cover Types
	Pesek, Ondrej; Segal-Rozenhaimer, Michal; Karnieli, Arnon
通讯作者	Pesek, O
来源期刊	REMOTE SENSING
EISSN	2072-4292
出版年	2022
卷号	14 期号:20
英文摘要	In most parts of the electromagnetic spectrum, solar radiation cannot penetrate clouds. Therefore, cloud detection and masking are essential in image preprocessing for observing the Earth and analyzing its properties. Because clouds vary in size, shape, and structure, an accurate algorithm is required for removing them from the area of interest. This task is usually more challenging over bright surfaces such as exposed sunny deserts or snow than over water bodies or vegetated surfaces. The overarching goal of the current study is to explore and compare the performance of three Convolutional Neural Network architectures (U-Net, SegNet, and DeepLab) for detecting clouds in the VEN mu S satellite images. To fulfil this goal, three VEN mu S tiles in Israel were selected. The tiles represent different land-use and cover categories, including vegetated, urban, agricultural, and arid areas, as well as water bodies, with a special focus on bright desert surfaces. Additionally, the study examines the effect of various channel inputs, exploring possibilities of broader usage of these architectures for different data sources. It was found that among the tested architectures, U-Net performs the best in most settings. Its results on a simple RGB-based dataset indicate its potential value for any satellite system screening, at least in the visible spectrum. It is concluded that all of the tested architectures outperform the current VEN mu S cloud-masking algorithm by lowering the false positive detection ratio by tens of percents, and should be considered an alternative by any user dealing with cloud-corrupted scenes.
英文关键词	remote sensing CNN artificial neural network deep learning semantic segmentation
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000873687200001
WOS关键词	REMOTE-SENSING IMAGES ; SHADOW DETECTION ; AUTOMATED CLOUD ; SNOW DETECTION
WOS类目	Environmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS研究方向	Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/394217
推荐引用方式 GB/T 7714	Pesek, Ondrej,Segal-Rozenhaimer, Michal,Karnieli, Arnon. Using Convolutional Neural Networks for Cloud Detection on VENμS Images over Multiple Land-Cover Types[J],2022,14(20).
APA	Pesek, Ondrej,Segal-Rozenhaimer, Michal,&Karnieli, Arnon.(2022).Using Convolutional Neural Networks for Cloud Detection on VENμS Images over Multiple Land-Cover Types.REMOTE SENSING,14(20).
MLA	Pesek, Ondrej,et al."Using Convolutional Neural Networks for Cloud Detection on VENμS Images over Multiple Land-Cover Types".REMOTE SENSING 14.20(2022).