Articles | Volume 15, issue 11
Atmos. Meas. Tech., 15, 3629–3639, 2022
Atmos. Meas. Tech., 15, 3629–3639, 2022
Research article
16 Jun 2022
Research article | 16 Jun 2022

An all-sky camera image classification method using cloud cover features

Xiaotong Li et al.

Related subject area

Subject: Clouds | Technique: Remote Sensing | Topic: Data Processing and Information Retrieval
Determination of atmospheric column condensate using active and passive remote sensing technology
Huige Di, Yun Yuan, Qing Yan, Wenhui Xin, Shichun Li, Jun Wang, Yufeng Wang, Lei Zhang, and Dengxin Hua
Atmos. Meas. Tech., 15, 3555–3567,,, 2022
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Improving discrimination between clouds and optically thick aerosol plumes in geostationary satellite data
Daniel Robbins, Caroline Poulsen, Steven Siems, and Simon Proud
Atmos. Meas. Tech., 15, 3031–3051,,, 2022
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Towards the use of conservative thermodynamic variables in data assimilation: a case study using ground-based microwave radiometer measurements
Pascal Marquet, Pauline Martinet, Jean-François Mahfouf, Alina Lavinia Barbu, and Benjamin Ménétrier
Atmos. Meas. Tech., 15, 2021–2035,,, 2022
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Empirical model of multiple-scattering effect on single-wavelength lidar data of aerosols and clouds
Valery Shcherbakov, Frédéric Szczap, Alaa Alkasem, Guillaume Mioche, and Céline Cornet
Atmos. Meas. Tech., 15, 1729–1754,,, 2022
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Analytic characterization of random errors in spectral dual-polarized cloud radar observations
Alexander Myagkov and Davide Ori
Atmos. Meas. Tech., 15, 1333–1354,,, 2022
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Cited articles

Calbo, J., and Sabburg, J.: Feature extraction from whole-sky ground-based images for cloud-type recognition, J. Atmos. Ocean. Tech., 25, 3–14,, 2008. 
Cao, Z. H., Hao, J. X., Feng, L., Jones, H. R. A., Li, J., and Xu, J.: Data processing and data products from 2017 to 2019 campaign of astronomical site testing at Ali, Daocheng and Muztagh-ata, Res. Astron. Astrophys.,, 20, 082, 2020. 
Cristianini, N. and Shawe-Taylor, J.: An introduction to support vector machines and other kernel-based learning methods, Cambridge University Press, Cambridge, UK,, 2000. 
Dev, S., Lee, Y. H., and Winkler, S.: Categorization of cloud image patches using an improved texton-based approach, in: 2015 IEEE Image Proc., Quebec City, QC, Canada, 27–30 September 2015, 422–426,, 2015. 
Esteves, J., Cao, Y., Silva, N. P. D., Pestana, R., and Wang, Z.: Identification of clouds using an all-sky imager, in: 2021 IEEE Madrid PowerTech, Madrid, Spain, 28 June–2 July 2021, 1–5,, 2021. 
Short summary
The all-sky camera images can reflect the local cloud cover, which is considerable for astronomical observatory site selection. Therefore, the realization of automatic classification of the images is very important. In this paper, three cloud cover features are proposed to classify the images. The proposed method is evaluated on a large dataset, and the method achieves an accuracy of 96.58 % and F1_score of 96.24 %, which greatly improves the efficiency of automatic processing of the images.