Abstract:

Auroral light is the result of charged particles interacting with the magnetosphere and ionosphere.
Proper classification of complex morphological all-sky auroral images is meaningful for studying the relationship
between electromagnetic activity and energy coupling. To address these issues, a method of deep
learning based on a convolutional neural network was proposed to explore the feature space of auroral data
and to achieve automatic auroral recognition. The representation method was used in automatic recognition
of four primary categories of aurora observed in 2003 at the Yellow River Station. The supervised classification
accuracy rates on labeled data between dataset1 and dataset2 were 93.17% and 91.5%, respectively. The
occurrence distributions of the four categories obtained through automatic classification of data from
2004–2009, were consistent with the spectral energy distribution excited by three bands. The experimental
results showed that the presented representation method is effective for automatic auroral image recognition.

Key words: aurora, convolutional neural network, classification