Articles | Volume 7, issue 1
https://doi.org/10.5194/gi-7-113-2018
https://doi.org/10.5194/gi-7-113-2018
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 Yang and Ze-Jun Hu

Related authors

Prediction and variation of the auroral oval boundary based on a deep learning model and space physical parameters
Yiyuan Han, Bing Han, Zejun Hu, Xinbo Gao, Lixia Zhang, Huigen Yang, and Bin Li
Nonlin. Processes Geophys., 27, 11–22, https://doi.org/10.5194/npg-27-11-2020,https://doi.org/10.5194/npg-27-11-2020, 2020
Short summary

Related subject area

Image processing
Automatic detection of calving events from time-lapse imagery at Tunabreen, Svalbard
Dorothée Vallot, Sigit Adinugroho, Robin Strand, Penelope How, Rickard Pettersson, Douglas I. Benn, and Nicholas R. J. Hulton
Geosci. Instrum. Method. Data Syst., 8, 113–127, https://doi.org/10.5194/gi-8-113-2019,https://doi.org/10.5194/gi-8-113-2019, 2019
Short summary
Integration of remote sensing and geographic information systems for geological fault detection on the island of Crete, Greece
Mohamed Elhag and Dalal Alshamsi
Geosci. Instrum. Method. Data Syst., 8, 45–54, https://doi.org/10.5194/gi-8-45-2019,https://doi.org/10.5194/gi-8-45-2019, 2019
Short summary
Consideration of NDVI thematic changes in density analysis and floristic composition of Wadi Yalamlam, Saudi Arabia
Amal Y. Aldhebiani, Mohamed Elhag, Ahmad K. Hegazy, Hanaa K. Galal, and Norah S. Mufareh
Geosci. Instrum. Method. Data Syst., 7, 297–306, https://doi.org/10.5194/gi-7-297-2018,https://doi.org/10.5194/gi-7-297-2018, 2018
Short summary
Precise DEM extraction from Svalbard using 1936 high oblique imagery
Luc Girod, Niels Ivar Nielsen, Frédérique Couderette, Christopher Nuth, and Andreas Kääb
Geosci. Instrum. Method. Data Syst., 7, 277–288, https://doi.org/10.5194/gi-7-277-2018,https://doi.org/10.5194/gi-7-277-2018, 2018
Short summary
Understanding of morphometric features for adequate water resource management in arid environments
Mohamed Elhag, Hanaa K. Galal, and Haneen Alsubaie
Geosci. Instrum. Method. Data Syst., 6, 293–300, https://doi.org/10.5194/gi-6-293-2017,https://doi.org/10.5194/gi-6-293-2017, 2017
Short summary

Cited articles

Akasofu, S. I. and Kan, J. R.: Dayside and nightside auroral arc systems, Geophys. Res. Lett., 7, 753–756, 1980. 
Chaston, C. C., Carlson, C. W., McFadden, J. P., Ergun, R. E., and Strangeway, R. J.: How important are dispersive Alfvén waves for auroral particle acceleration?, Geophys. Res. Lett., 34, L07101, https://doi.org/10.1029/2006GL029144, 2007. 
Ebihara, Y., Tanaka, Y.-M., Takasaki, S., Weatherwax, A. T., and Taguchi, M.: Quasi-stationary auroral patches observed at the South Pole Station, J. Geophys. Res., 112, A01201, https://doi.org/10.1029/2006JA012087, 2007. 
Han, D. S., Chen, X., Liu, J., Qiu, Q., Keika, K., Hu, Z. J., Liu, J. M., Hu, H. Q., and Yang, H. G.: An extensive survey of dayside diffuse aurora based on optical observations at Yellow River Station, J. Geophys. Res.-Space Phys., 120, 7447–7465, 2015. 
Han, D. S., Nishimura, Y., Lyons, L. R., Hu, H. Q., and Yang, H. G.: Throat aurora: The ionospheric signature of magnetosheath particles penetrating into the magnetosphere, Geophys. Res. Lett., 43, 1819–1827, https://doi.org/10.1002/2016GL068181, 2016. 
Download
Short summary
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.