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SIGNN – Star Identification Using Graph Neural Networks

Floyd Hepburn-Dickins, Mark Jones Orcid Logo, Mike Edwards Orcid Logo, Jay Paul Morgan Orcid Logo, Steve Bell

2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Pages: 9063 - 9072

Swansea University Authors: Floyd Hepburn-Dickins, Mark Jones Orcid Logo, Mike Edwards Orcid Logo, Jay Paul Morgan Orcid Logo

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DOI (Published version): 10.1109/wacv61041.2025.00878

Abstract

As a solution for the lost-in-space star identification problem we present Star Identification using Graph Neural Network (SIGNN), a novel approach using Graph Attention Networks. By representing the celestial sphere as a graph data structure, created from the ESA's Hipparcos catalogue, we are...

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Published in: 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Published: IEEE 2025
Online Access: https://doi.org/10.1109/wacv61041.2025.00878
URI: https://cronfa.swan.ac.uk/Record/cronfa68091
first_indexed 2024-10-29T11:03:15Z
last_indexed 2025-05-03T04:40:05Z
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spelling 2025-05-02T13:49:14.6598660 v2 68091 2024-10-29 SIGNN – Star Identification Using Graph Neural Networks d8ecf05934e394b7bd020a2ce2c11d0c Floyd Hepburn-Dickins Floyd Hepburn-Dickins true false 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false 684864a1ce01c3d774e83ed55e41770e 0000-0003-3367-969X Mike Edwards Mike Edwards true false df9a27bcf77b4769c2ebbb702b587491 0000-0003-3719-362X Jay Paul Morgan Jay Paul Morgan true false 2024-10-29 MACS As a solution for the lost-in-space star identification problem we present Star Identification using Graph Neural Network (SIGNN), a novel approach using Graph Attention Networks. By representing the celestial sphere as a graph data structure, created from the ESA's Hipparcos catalogue, we are able to accurately capture the rich information and relationships within local star fields. Graph learning techniques allow our model to aggregate information and learn the relative importance of the nodes and structure within each stars local neighbourhood to it's identification. This approach, combined with our parametric data-generation and noise simulation, allows us to train a highly robust model capable of accurate star identification even under intensive noise, outperforming existing methods. Code and generation techniques will be available on github.com. Conference Paper/Proceeding/Abstract 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 9063 9072 IEEE 26 2 2025 2025-02-26 10.1109/wacv61041.2025.00878 https://doi.org/10.1109/wacv61041.2025.00878 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Not Required EPSRC, EP/S021892/1 2025-05-02T13:49:14.6598660 2024-10-29T10:58:16.3133961 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Floyd Hepburn-Dickins 1 Mark Jones 0000-0001-8991-1190 2 Mike Edwards 0000-0003-3367-969X 3 Jay Paul Morgan 0000-0003-3719-362X 4 Steve Bell 5 68091__33069__32352d3a6aeb411d810311c54a810d80.pdf 68091.pdf 2024-12-06T10:04:06.7580325 Output 1205545 application/pdf Accepted Manuscript true Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. true eng https://creativecommons.org/licenses/by/4.0/deed.en
title SIGNN – Star Identification Using Graph Neural Networks
spellingShingle SIGNN – Star Identification Using Graph Neural Networks
Floyd Hepburn-Dickins
Mark Jones
Mike Edwards
Jay Paul Morgan
title_short SIGNN – Star Identification Using Graph Neural Networks
title_full SIGNN – Star Identification Using Graph Neural Networks
title_fullStr SIGNN – Star Identification Using Graph Neural Networks
title_full_unstemmed SIGNN – Star Identification Using Graph Neural Networks
title_sort SIGNN – Star Identification Using Graph Neural Networks
author_id_str_mv d8ecf05934e394b7bd020a2ce2c11d0c
2e1030b6e14fc9debd5d5ae7cc335562
684864a1ce01c3d774e83ed55e41770e
df9a27bcf77b4769c2ebbb702b587491
author_id_fullname_str_mv d8ecf05934e394b7bd020a2ce2c11d0c_***_Floyd Hepburn-Dickins
2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones
684864a1ce01c3d774e83ed55e41770e_***_Mike Edwards
df9a27bcf77b4769c2ebbb702b587491_***_Jay Paul Morgan
author Floyd Hepburn-Dickins
Mark Jones
Mike Edwards
Jay Paul Morgan
author2 Floyd Hepburn-Dickins
Mark Jones
Mike Edwards
Jay Paul Morgan
Steve Bell
format Conference Paper/Proceeding/Abstract
container_title 2025 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
container_start_page 9063
publishDate 2025
institution Swansea University
doi_str_mv 10.1109/wacv61041.2025.00878
publisher IEEE
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 https://doi.org/10.1109/wacv61041.2025.00878
document_store_str 1
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description As a solution for the lost-in-space star identification problem we present Star Identification using Graph Neural Network (SIGNN), a novel approach using Graph Attention Networks. By representing the celestial sphere as a graph data structure, created from the ESA's Hipparcos catalogue, we are able to accurately capture the rich information and relationships within local star fields. Graph learning techniques allow our model to aggregate information and learn the relative importance of the nodes and structure within each stars local neighbourhood to it's identification. This approach, combined with our parametric data-generation and noise simulation, allows us to train a highly robust model capable of accurate star identification even under intensive noise, outperforming existing methods. Code and generation techniques will be available on github.com.
published_date 2025-02-26T05:24:32Z
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score 11.089386

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