dc.contributor.author | Kvammen, Andreas | |
dc.contributor.author | Wickstrøm, Kristoffer Knutsen | |
dc.contributor.author | McKay, Derek | |
dc.contributor.author | Partamies, Noora | |
dc.date.accessioned | 2020-10-29T09:29:21Z | |
dc.date.available | 2020-10-29T09:29:21Z | |
dc.date.issued | 2020-10-05 | |
dc.description.abstract | 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 <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.citation | Kvammen A, Wickstrøm KK, McKay D, Partamies N. Auroral Image Classification With Deep Neural Networks. Journal of Geophysical Research (JGR): Space Physics. 2020;125 | en_US |
dc.identifier.cristinID | FRIDAID 1841229 | |
dc.identifier.doi | 10.1029/2020JA027808 | |
dc.identifier.issn | 2169-9380 | |
dc.identifier.issn | 2169-9402 | |
dc.identifier.uri | https://hdl.handle.net/10037/19703 | |
dc.language.iso | eng | en_US |
dc.publisher | Wiley | en_US |
dc.relation.ispartof | Kwammen, 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.journal | Journal of Geophysical Research (JGR): Space Physics | |
dc.rights.accessRights | openAccess | en_US |
dc.rights.holder | Copyright 2020 The Author(s) | en_US |
dc.subject | VDP::Mathematics and natural science: 400::Physics: 430 | en_US |
dc.subject | VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430 | en_US |
dc.title | Auroral Image Classification With Deep Neural Networks | en_US |
dc.type.version | publishedVersion | en_US |
dc.type | Journal article | en_US |
dc.type | Tidsskriftartikkel | en_US |
dc.type | Peer reviewed | en_US |