Auroral Image Processing Techniques - Machine Learning Classification and Multi-Viewpoint Analysis

• Main Author: Kvammen, Andreas
• Format: Doctoral or Postdoctoral Thesis
• Language: English
• Published: UiT Norges arktiske universitet 2021
• Subjects: VDP::Mathematics and natural science: 400::Physics: 430::Space and plasma physics: 437; VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430::Rom- og plasmafysikk: 437; VDP::Mathematics and natural science: 400::Information and communication science: 420::Simulation; visualization; signal processing; image processing: 429; VDP::Matematikk og Naturvitenskap: 400::Informasjons- og kommunikasjonsvitenskap: 420::Simulering; visualisering; signalbehandling; bildeanalyse: 429; Antarctic; Arctic; Antarc*
• Online Access: https://hdl.handle.net/10037/22584

Every year, millions of scientific images are acquired in order to study the auroral phenomena. The accumulated data contain a vast amount of untapped information that can be used in auroral science. Yet, auroral research has traditionally been focused on case studies, where one or a few auroral events have been investigated and explained in detail. Consequently, theories have often been developed on the basis of limited data sets, which can possibly be biased in location, spatial resolution or temporal resolution. Advances in technology and data processing now allow for acquisition and analysis of large image data sets. These tools have made it feasible to perform statistical studies based on auroral data from numerous events, varying geophysical conditions and multiple locations in the Arctic and Antarctic. Such studies require reliable auroral image processing techniques to organize, extract and represent the auroral information in a scientifically rigorous manner, preferably with a minimal amount of user interaction. This dissertation focuses on two such branches of image processing techniques: machine learning classification and multi-viewpoint analysis. Machine learning classification: This thesis provides an in-depth description on the implementation of machine learning methods for auroral image classification; from raw images to labeled data. The main conclusion of this work is that convolutional neural networks stand out as a particularly suitable classifier for auroral image data, achieving up to 91 % average class-wise accuracy. A major challenge is that most auroral images have an ambiguous auroral form. These images can not be readily labeled without establishing an auroral morphology, where each class is clearly defined. Multi-viewpoint analysis: Three multi-viewpoint analysis techniques are evaluated and described in this work: triangulation, shell-projection and 3-D reconstruction. These techniques are used for estimating the volume distribution of artificially induced aurora and the height and ...