Replication Data for: Auroral Image Classification with Deep Neural Networks ...

• Main Authors: Kvammen, Andreas, Wickstrøm, Kristoffer, McKay, Derek, Partamies, Noora
• Format: Dataset
• Language: unknown
• Published: DataverseNO 2020
• Subjects: Antarc*; Antarctic; Arctic
• Online Access: https://dx.doi.org/10.18710/ssa38j; https://dataverse.no/citation?persistentId=doi:10.18710/SSA38J

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-bands, discrete, patchy, edge and clear-faint. Five different deep neural network architectures have been tested along with the well known classification methods; k nearest neighbor (KNN) and support vector machine (SVM). A set of clean nighttime color auroral images, without ambiguous auroral forms, moonlight, twilight, ...