Conference Paper/Proceeding/Abstract 219 views 19 downloads
Noise Robustness of Data-Driven Star Classification
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
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...
| Published in: | Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods |
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| ISBN: | 978-989-758-626-2 |
| ISSN: | 2184-4313 |
| Published: |
Lisbon, Portugal
SCITEPRESS - Science and Technology Publications
2023
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa63778 |
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| 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 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. |
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| Keywords: |
Neural Networks, Star-Pattern Recognition, Data Generation |
| College: |
Faculty of Science and Engineering |
| Funders: |
EPSRC Centre for Doctoral Training in Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems, EP/S021892/1. |
| Start Page: |
176 |
| End Page: |
184 |