Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Automatic Classification of All-sky Nighttime Cloud Images Based on Machine Learning

Xin Zhong
,
Fujia Du
* ORCID logo ,
Yi Hu
,
Xu Hou
ORCID logo ,
Zonghong Zhu
,
Xiaogang Zheng
ORCID logo ,
Kang Huang
ORCID logo ,
Zhimin Ren
,
Yonghui Hou
Version 1 : Received: 13 March 2024 / Approved: 13 March 2024 / Online: 13 March 2024 (12:40:29 CET)

A peer-reviewed article of this Preprint also exists.

Zhong, X.; Du, F.; Hu, Y.; Hou, X.; Zhu, Z.; Zheng, X.; Huang, K.; Ren, Z.; Hou, Y. Automatic Classification of All-Sky Nighttime Cloud Images Based on Machine Learning. Electronics 2024, 13, 1503. Zhong, X.; Du, F.; Hu, Y.; Hou, X.; Zhu, Z.; Zheng, X.; Huang, K.; Ren, Z.; Hou, Y. Automatic Classification of All-Sky Nighttime Cloud Images Based on Machine Learning. Electronics 2024, 13, 1503.

Abstract

Cloud-induced atmospheric extinction and occlusion have a major effect on the effectiveness and quality of telescope observations. Real-time cloud cover distribution and long-term statistical data are essential for astronomical siting and telescope operations. At ground-based astronomical telescope sites, cloud cover distribution is currently analyzed using manual observation methods. However, the main disadvantages of manual observation methods are human subjective, heavy workloads and poor real-time performance. Therefore, a real-time automatic cloud images classification method is desperately needed. This paper presents a new cloud identification method, which is named PSO+XGBoost model by combining eXtreme Gradient Boosting (XGBoost) and Particle Swarm Optimization (PSO). The entire cloud image is divided into 37 sub-regions in order to more precisely identify the distribution of the clouds. 19 features are then extracted, including the sky background, star density, lighting conditions, and subregion grayscale values. The experimental results have shown that the overall classification accuracy of 96.91% and our model is able to outperform several state-of-the-art baseline methods. Our approach achieves high accuracy in comparison with the manual observation methods. Moreover, this method meets telescope real-time scheduling requirements.

Keywords

nighttime cloud image; observational astronomy; all-sky cameras; PSO; XGBoost

Subject

Computer Science and Mathematics, Computer Vision and Graphics

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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