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Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network
Ma, Nan1; Sun, Lin2; Zhou, Chenghu3; He, Yawen4
2021-08-01
Source PublicationREMOTE SENSING
Volume13Issue:16Pages:20
Corresponding AuthorSun, Lin(sunlin6@126.com)
AbstractAutomatic cloud detection in remote sensing images is of great significance. Deep-learning-based methods can achieve cloud detection with high accuracy; however, network training heavily relies on a large number of labels. Manually labelling pixel-wise level cloud and non-cloud annotations for many remote sensing images is laborious and requires expert-level knowledge. Different types of satellite images cannot share a set of training data, due to the difference in spectral range and spatial resolution between them. Hence, labelled samples in each upcoming satellite image are required to train a new deep-learning-based model. In order to overcome such a limitation, a novel cloud detection algorithm based on a spectral library and convolutional neural network (CD-SLCNN) was proposed in this paper. In this method, the residual learning and one-dimensional CNN (Res-1D-CNN) was used to accurately capture the spectral information of the pixels based on the prior spectral library, effectively preventing errors due to the uncertainties in thin clouds, broken clouds, and clear-sky pixels during remote sensing interpretation. Benefiting from data simulation, the method is suitable for the cloud detection of different types of multispectral data. A total of 62 Landsat-8 Operational Land Imagers (OLI), 25 Moderate Resolution Imaging Spectroradiometers (MODIS), and 20 Sentinel-2 satellite images acquired at different times and over different types of underlying surfaces, such as a high vegetation coverage, urban area, bare soil, water, and mountains, were used for cloud detection validation and quantitative analysis, and the cloud detection results were compared with the results from the function of the mask, MODIS cloud mask, support vector machine, and random forest. The comparison revealed that the CD-SLCNN method achieved the best performance, with a higher overall accuracy (95.6%, 95.36%, 94.27%) and mean intersection over union (77.82%, 77.94%, 77.23%) on the Landsat-8 OLI, MODIS, and Sentinel-2 data, respectively. The CD-SLCNN algorithm produced consistent results with a more accurate cloud contour on thick, thin, and broken clouds over a diverse underlying surface, and had a stable performance regarding bright surfaces, such as buildings, ice, and snow.
KeywordASTER spectral library hyperspectral data 1D convolutional neural network cloud detection data simulation multi-satellite remote sensing images
DOI10.3390/rs13163319
WOS KeywordSNOW DETECTION ; LANDSAT DATA ; PART I ; DEEP ; SHADOW ; CLASSIFICATION ; VALIDATION ; MODIS
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[41171408] ; National Natural Science Foundation of China[41976184] ; Natural Science Foundation of Shandong Province[ZR201702210379]
Funding OrganizationNational Natural Science Foundation of China ; Natural Science Foundation of Shandong Province
WOS Research AreaEnvironmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology
WOS SubjectEnvironmental Sciences ; Geosciences, Multidisciplinary ; Remote Sensing ; Imaging Science & Photographic Technology
WOS IDWOS:000690075200001
PublisherMDPI
Citation statistics
Document Type期刊论文
Identifierhttp://ir.igsnrr.ac.cn/handle/311030/165122
Collection中国科学院地理科学与资源研究所
Corresponding AuthorSun, Lin
Affiliation1.China Univ Petr, Sch Geosci, Qingdao 266580, Peoples R China
2.Shandong Univ Sci & Technol, Coll Geodesy & Geomat, Qingdao 266590, Peoples R China
3.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China
4.China Univ Petr, Coll Oceanog & Space Informat, Qingdao 266580, Peoples R China
Recommended Citation
GB/T 7714
Ma, Nan,Sun, Lin,Zhou, Chenghu,et al. Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network[J]. REMOTE SENSING,2021,13(16):20.
APA Ma, Nan,Sun, Lin,Zhou, Chenghu,&He, Yawen.(2021).Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network.REMOTE SENSING,13(16),20.
MLA Ma, Nan,et al."Cloud Detection Algorithm for Multi-Satellite Remote Sensing Imagery Based on a Spectral Library and 1D Convolutional Neural Network".REMOTE SENSING 13.16(2021):20.
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