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Noise Robustness of Data-Driven Star Classification

Floyd Hepburn-Dickins, Mike Edwards Orcid Logo

Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods, Volume: 1, Pages: 176 - 184

Swansea University Authors: Floyd Hepburn-Dickins, Mike Edwards Orcid Logo

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DOI (Published version): 10.5220/0011804000003411

Abstract

Celestial navigation has fallen into the background in light of newer technologies such as global positioning systems, but research into its core component, star pattern recognition, has remained an active area of study. We examine these methods and the viability of a data-driven approach to detecti...

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Published in: Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods
ISBN: 978-989-758-626-2
ISSN: 2184-4313
Published: Lisbon, Portugal SCITEPRESS - Science and Technology Publications 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa63778
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spelling v2 63778 2023-07-05 Noise Robustness of Data-Driven Star Classification d8ecf05934e394b7bd020a2ce2c11d0c Floyd Hepburn-Dickins Floyd Hepburn-Dickins true false 684864a1ce01c3d774e83ed55e41770e 0000-0003-3367-969X Mike Edwards Mike Edwards true false 2023-07-05 FGSEN Celestial navigation has fallen into the background in light of newer technologies such as global positioning systems, but research into its core component, star pattern recognition, has remained an active area of study. We examine these methods and the viability of a data-driven approach to detecting and recognising stars within images taken from the Earth’s surface. We show that synthetic datasets, necessary due to a lack of labelled real image datasets, are able to appropriately simulate the night sky from a terrestrial perspective and that such an implementation can successfully perform star patter recognition in this domain. In this work we apply three kinds of noise in a parametric fashion; positional noise, false star noise, and dropped star noise. Results show that a pattern mining approach can accurately identify stars from night sky images and our results show the impact of the above noise types on classifier performance. Conference Paper/Proceeding/Abstract Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods 1 176 184 SCITEPRESS - Science and Technology Publications Lisbon, Portugal 978-989-758-626-2 2184-4313 Neural Networks, Star-Pattern Recognition, Data Generation 3 3 2023 2023-03-03 10.5220/0011804000003411 http://dx.doi.org/10.5220/0011804000003411 COLLEGE NANME Science and Engineering - Faculty COLLEGE CODE FGSEN Swansea University Not Required EPSRC Centre for Doctoral Training in Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems, EP/S021892/1. EP/S021892/1 2023-12-08T13:09:38.4893779 2023-07-05T09:58:05.3638922 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Floyd Hepburn-Dickins 1 Mike Edwards 0000-0003-3367-969X 2 63778__28040__f3e19f378f154f6cbfef271b5d4be554.pdf 118040.pdf 2023-07-05T10:17:31.7211555 Output 1335156 application/pdf Accepted Manuscript true Attribution-NonCommercial-NoDerivs 4.0 International true eng https://creativecommons.org/licenses/by-nc-nd/4.0/deed.en
title Noise Robustness of Data-Driven Star Classification
spellingShingle Noise Robustness of Data-Driven Star Classification
Floyd Hepburn-Dickins
Mike Edwards
title_short Noise Robustness of Data-Driven Star Classification
title_full Noise Robustness of Data-Driven Star Classification
title_fullStr Noise Robustness of Data-Driven Star Classification
title_full_unstemmed Noise Robustness of Data-Driven Star Classification
title_sort Noise Robustness of Data-Driven Star Classification
author_id_str_mv d8ecf05934e394b7bd020a2ce2c11d0c
684864a1ce01c3d774e83ed55e41770e
author_id_fullname_str_mv d8ecf05934e394b7bd020a2ce2c11d0c_***_Floyd Hepburn-Dickins
684864a1ce01c3d774e83ed55e41770e_***_Mike Edwards
author Floyd Hepburn-Dickins
Mike Edwards
author2 Floyd Hepburn-Dickins
Mike Edwards
format Conference Paper/Proceeding/Abstract
container_title Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods
container_volume 1
container_start_page 176
publishDate 2023
institution Swansea University
isbn 978-989-758-626-2
issn 2184-4313
doi_str_mv 10.5220/0011804000003411
publisher SCITEPRESS - Science and Technology Publications
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
url http://dx.doi.org/10.5220/0011804000003411
document_store_str 1
active_str 0
description Celestial navigation has fallen into the background in light of newer technologies such as global positioning systems, but research into its core component, star pattern recognition, has remained an active area of study. We examine these methods and the viability of a data-driven approach to detecting and recognising stars within images taken from the Earth’s surface. We show that synthetic datasets, necessary due to a lack of labelled real image datasets, are able to appropriately simulate the night sky from a terrestrial perspective and that such an implementation can successfully perform star patter recognition in this domain. In this work we apply three kinds of noise in a parametric fashion; positional noise, false star noise, and dropped star noise. Results show that a pattern mining approach can accurately identify stars from night sky images and our results show the impact of the above noise types on classifier performance.
published_date 2023-03-03T13:09:38Z
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score 11.01628

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