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Geoscientific Instrumentation, Methods and Data Systems An interactive open-access journal of the European Geosciences Union
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Volume 7, issue 1 | Copyright
Geosci. Instrum. Method. Data Syst., 7, 113-122, 2018
https://doi.org/10.5194/gi-7-113-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 20 Mar 2018

Research article | 20 Mar 2018

A comparative study of auroral morphology distribution between the Northern and Southern Hemisphere based on automatic classification

Qiuju Yang1 and Ze-Jun Hu2 Qiuju Yang and Ze-Jun Hu
  • 1School of Physics and Information Technology, Shaanxi Normal University, Xi'an, 710119, China
  • 2SOA Key Laboratory for Polar Science, Polar Research Institute of China, Shanghai, 200136, China

Abstract. Aurora is a very important geophysical phenomenon in the high latitudes of Arctic and Antarctic regions, and it is important to make a comparative study of the auroral morphology between the two hemispheres. Based on the morphological characteristics of the four labeled dayside discrete auroral types (auroral arc, drapery corona, radial corona and hot-spot aurora) on the 8001 dayside auroral images at the Chinese Arctic Yellow River Station in 2003, and by extracting the local binary pattern (LBP) features and using a k-nearest classifier, this paper performs an automatic classification of the 65361 auroral images of the Chinese Arctic Yellow River Station during 2004–2009 and the 39335 auroral images of the South Pole Station between 2003 and 2005. Finally, it obtains the occurrence distribution of the dayside auroral morphology in the Northern and Southern Hemisphere. The statistical results indicate that the four dayside discrete auroral types present a similar occurrence distribution between the two stations. To the best of our knowledge, we are the first to report statistical comparative results of dayside auroral morphology distribution between the Northern and Southern Hemisphere.

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Based on the morphological characteristics of the four dayside auroral types on images at the Chinese Arctic Yellow River Station (YRS), and by extracting the local binary pattern features and using k-nearest classifier, we make an automatic classification to the auroral images of the YRS and the South Pole Station and obtain the occurrence distribution of the dayside aurora morphology. The results indicate that these auroral types present similar occurrence distributions in the two stations.
Based on the morphological characteristics of the four dayside auroral types on images at the...
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