Auroral breakup detection in all-sky images by unsupervised learning

Due to a large number of automatic auroral camera systems on the ground, the image data analysis requires more efficiency than what a human expert visual inspection can provide. Furthermore, there is no solid consensus on how many different types or shapes exist in auroral displays. We report the fi...

Full description

Bibliographic Details
Main Authors: Partamies, Noora, Dol, Bas, Teissier, Vincent, Juusola, Liisa, Syrjäsuo, Mikko, Mulders, Hjalmar
Format: Article in Journal/Newspaper
Language:English
Published: Copernicus Publications 2023
Subjects:
Online Access:https://doi.org/10.5194/egusphere-2023-2857
https://noa.gwlb.de/receive/cop_mods_00070166
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00068522/egusphere-2023-2857.pdf
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2857/egusphere-2023-2857.pdf
id ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00070166
record_format openpolar
spelling ftnonlinearchiv:oai:noa.gwlb.de:cop_mods_00070166 2023-12-31T10:04:11+01:00 Auroral breakup detection in all-sky images by unsupervised learning Partamies, Noora Dol, Bas Teissier, Vincent Juusola, Liisa Syrjäsuo, Mikko Mulders, Hjalmar 2023-11 electronic https://doi.org/10.5194/egusphere-2023-2857 https://noa.gwlb.de/receive/cop_mods_00070166 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00068522/egusphere-2023-2857.pdf https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2857/egusphere-2023-2857.pdf eng eng Copernicus Publications https://doi.org/10.5194/egusphere-2023-2857 https://noa.gwlb.de/receive/cop_mods_00070166 https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00068522/egusphere-2023-2857.pdf https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2857/egusphere-2023-2857.pdf https://creativecommons.org/licenses/by/4.0/ uneingeschränkt info:eu-repo/semantics/openAccess article Verlagsveröffentlichung article Text doc-type:article 2023 ftnonlinearchiv https://doi.org/10.5194/egusphere-2023-2857 2023-12-04T00:22:44Z Due to a large number of automatic auroral camera systems on the ground, the image data analysis requires more efficiency than what a human expert visual inspection can provide. Furthermore, there is no solid consensus on how many different types or shapes exist in auroral displays. We report the first attempt to classify auroral morphological forms by unsupervised learning method on an image set that contains both nightside and dayside aurora. We used six months of full-colour auroral all-sky images captured at a high-arctic observatory on Svalbard, Norway, in 2019–2020. The selection of images containing aurora was performed manually. These images were then input to a convolutional neural network called SimCLR for feature extraction. The clustered and fused features resulted in 37 auroral morphological classes. In the classification of auroral image data with two different time resolutions we found that the occurrence of eight morphological classes strongly increased when the image cadence was high (24 seconds), while the occurrence of 13 morphological classes experienced little or no change with changes in input image cadence. We therefore investigated the temporal evolution of the group of eight "active auroral classes". Time periods for which "active auroral classes" persisted for longer than two consecutive images with maximum cadence of six minutes coincided with ground-magnetic deflections and their occurrence was found clustered around the magnetic midnight. The active auroral onsets typically included vortical auroral structures and equivalent current patterns typical for substorms. Our findings therefore suggest that our unsupervised image classification method can be used to detect auroral breakups in ground-based image datasets with a temporal accuracy determined by the image cadence. Article in Journal/Newspaper Arctic Svalbard Niedersächsisches Online-Archiv NOA
institution Open Polar
collection Niedersächsisches Online-Archiv NOA
op_collection_id ftnonlinearchiv
language English
topic article
Verlagsveröffentlichung
spellingShingle article
Verlagsveröffentlichung
Partamies, Noora
Dol, Bas
Teissier, Vincent
Juusola, Liisa
Syrjäsuo, Mikko
Mulders, Hjalmar
Auroral breakup detection in all-sky images by unsupervised learning
topic_facet article
Verlagsveröffentlichung
description Due to a large number of automatic auroral camera systems on the ground, the image data analysis requires more efficiency than what a human expert visual inspection can provide. Furthermore, there is no solid consensus on how many different types or shapes exist in auroral displays. We report the first attempt to classify auroral morphological forms by unsupervised learning method on an image set that contains both nightside and dayside aurora. We used six months of full-colour auroral all-sky images captured at a high-arctic observatory on Svalbard, Norway, in 2019–2020. The selection of images containing aurora was performed manually. These images were then input to a convolutional neural network called SimCLR for feature extraction. The clustered and fused features resulted in 37 auroral morphological classes. In the classification of auroral image data with two different time resolutions we found that the occurrence of eight morphological classes strongly increased when the image cadence was high (24 seconds), while the occurrence of 13 morphological classes experienced little or no change with changes in input image cadence. We therefore investigated the temporal evolution of the group of eight "active auroral classes". Time periods for which "active auroral classes" persisted for longer than two consecutive images with maximum cadence of six minutes coincided with ground-magnetic deflections and their occurrence was found clustered around the magnetic midnight. The active auroral onsets typically included vortical auroral structures and equivalent current patterns typical for substorms. Our findings therefore suggest that our unsupervised image classification method can be used to detect auroral breakups in ground-based image datasets with a temporal accuracy determined by the image cadence.
format Article in Journal/Newspaper
author Partamies, Noora
Dol, Bas
Teissier, Vincent
Juusola, Liisa
Syrjäsuo, Mikko
Mulders, Hjalmar
author_facet Partamies, Noora
Dol, Bas
Teissier, Vincent
Juusola, Liisa
Syrjäsuo, Mikko
Mulders, Hjalmar
author_sort Partamies, Noora
title Auroral breakup detection in all-sky images by unsupervised learning
title_short Auroral breakup detection in all-sky images by unsupervised learning
title_full Auroral breakup detection in all-sky images by unsupervised learning
title_fullStr Auroral breakup detection in all-sky images by unsupervised learning
title_full_unstemmed Auroral breakup detection in all-sky images by unsupervised learning
title_sort auroral breakup detection in all-sky images by unsupervised learning
publisher Copernicus Publications
publishDate 2023
url https://doi.org/10.5194/egusphere-2023-2857
https://noa.gwlb.de/receive/cop_mods_00070166
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00068522/egusphere-2023-2857.pdf
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2857/egusphere-2023-2857.pdf
genre Arctic
Svalbard
genre_facet Arctic
Svalbard
op_relation https://doi.org/10.5194/egusphere-2023-2857
https://noa.gwlb.de/receive/cop_mods_00070166
https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00068522/egusphere-2023-2857.pdf
https://egusphere.copernicus.org/preprints/2023/egusphere-2023-2857/egusphere-2023-2857.pdf
op_rights https://creativecommons.org/licenses/by/4.0/
uneingeschränkt
info:eu-repo/semantics/openAccess
op_doi https://doi.org/10.5194/egusphere-2023-2857
_version_ 1786830148055597056