Results from a study of automatic aurora classification using machine learning techniques
are presented. The aurora is the manifestation of physical phenomena in the ionosphere-magnetosphere
environment. Automatic classification of millions of auroral images from the Arctic and Antarctic is
therefore an attractive tool for developing auroral statistics and for supporting scientists to study auroral
images in an objective, organized, and repeatable manner. Although previous studies have presented
tools for detecting aurora, there has been a lack of tools for classifying aurora into subclasses with a
high precision ( > 90%). This work considers seven auroral subclasses: breakup, colored, arcs, discrete,
patchy, edge, and faint. Six different deep neural network architectures have been tested along with the
well-known classification algorithms: k-nearest neighbor (KNN) and a support vector machine (SVM).
A set of clean nighttime color auroral images, without clearly ambiguous auroral forms, moonlight,
twilight, clouds, and so forth, were used for training and testing the classifiers. The deep neural networks
generally outperformed the KNN and SVM methods, and the ResNet-50 architecture achieved the highest
performance with an average classification precision of 92%.