Auroral Image Classification With Deep Neural Networks

• Published in: Journal of geophysical research. Space physics 2020-10, Vol.125 (10), p.n/a
• Main Authors: Kvammen, Andreas, Wickstrøm, Kristoffer, McKay, Derek, Partamies, Noora
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial neural networks; aurora classification; Auroras; automatic aurora labeling; Classification; Computer architecture; convolutional neural networks; deep neural networks; Fysikk: 430; Image classification; Ionosphere; labeled aurora data; Machine learning; Magnetospheres; Matematikk og Naturvitenskap: 400; Mathematics and natural science: 400; Neural networks; Physics: 430; Support vector machines; Twilight; VDP
• ISSN: 2169-9380; 2169-9402; 2169-9402
• DOI: 10.1029/2020JA027808

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%. Key Points Auroral images were labeled into seven classes and used to train and test machine learning classifiers The deep neural networks outperformed the k‐nearest neighbor and support vector machine classifiers The ResNet‐50 deep neural network had the highest performance and achieved 92% precision