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Unsupervised automatic classification of all-sky auroral images using deep clustering technology

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Abstract

Reasonable classification of aurora is of great significance to the study of the generation mechanism of aurora and the dynamic process of the magnetosphere boundary layer. Previous aurora classification studies, both manual and automatic, rely on experts’ visual inspection and manual labeling of part or all of the data. However, there is currently no consensus on aurora classification schemes. In this paper, an auroral image clustering network (AICNet) is proposed to unsupervised classification of all-sky images by grouping observations according to their morphological similarities. AICNet is fully automatic and requires no human supervision to tell the classification scheme or manually label samples. In the experiments, 4000 dayside all-sky auroral images captured at the Chinese Yellow River Station during 2003–2008 were considered. The images were clustered into two classes. Auroral morphology in the two clusters exhibits high intra-cluster similarity and low inter-cluster similarity. The temporal occurrence distributions illustrate that one cluster appears a double-peak distribution and mostly occurs in the afternoon, while the other cluster mostly occurs before and at noon. Experimental results demonstrate that AICNet can discover the internal structures of auroras and would greatly improve the efficiency of auroral morphology classification.

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (Grants 41504122 and 61571353), National Science Basic Research Plan in Shaanxi Province of China (Grant 2020JM-272), and Fundamental Research Funds for the Central Universities (Grant GK202103020). Auroral observations at Yellow River Station (YRS) are supported by CHINARE and provided by Polar Research Institute of China (http://www.chinare.org.cn:8000/uap/database).

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Authors and Affiliations

  1. School of Physics and Information Technology, Shaanxi Normal University, Xi’an, 710119, China

    Qiuju Yang & Chang Liu

  2. School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Jimin Liang

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  1. Qiuju Yang
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  2. Chang Liu
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  3. Jimin Liang
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Correspondence to Qiuju Yang.

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Communicated by: H. Babaie

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Yang, Q., Liu, C. & Liang, J. Unsupervised automatic classification of all-sky auroral images using deep clustering technology. Earth Sci Inform 14, 1327–1337 (2021). https://doi.org/10.1007/s12145-021-00634-1

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  • DOI: https://doi.org/10.1007/s12145-021-00634-1

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