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

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.