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dc.contributor.authorKvammen, Andreas
dc.contributor.authorWickstrøm, Kristoffer Knutsen
dc.contributor.authorMcKay, Derek
dc.contributor.authorPartamies, Noora
dc.date.accessioned2020-10-29T09:29:21Z
dc.date.available2020-10-29T09:29:21Z
dc.date.issued2020-10-05
dc.description.abstractResults 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 <i>millions</i> 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: <i>breakup, colored, arcs, discrete, patchy, edge</i>, and <i>faint</i>. 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%.en_US
dc.identifier.citationKvammen A, Wickstrøm KK, McKay D, Partamies N. Auroral Image Classification With Deep Neural Networks. Journal of Geophysical Research (JGR): Space Physics. 2020;125en_US
dc.identifier.cristinIDFRIDAID 1841229
dc.identifier.doi10.1029/2020JA027808
dc.identifier.issn2169-9380
dc.identifier.issn2169-9402
dc.identifier.urihttps://hdl.handle.net/10037/19703
dc.language.isoengen_US
dc.publisherWileyen_US
dc.relation.ispartofKwammen, A. (2021). Auroral Image Processing Techniques - Machine Learning Classification and Multi-Viewpoint Analysis. (Doctoral thesis). <a href=https://hdl.handle.net/10037/22584>https://hdl.handle.net/10037/22584</a>
dc.relation.journalJournal of Geophysical Research (JGR): Space Physics
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2020 The Author(s)en_US
dc.subjectVDP::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.titleAuroral Image Classification With Deep Neural Networksen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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