Deep Learning for Astronomical Object Classification: A Case Study

Ana Martinazzo, Mateus Espadoto, Nina S. T. Hirata

2020

Abstract

With the emergence of photometric surveys in astronomy, came the challenge of processing and understanding an enormous amount of image data. In this paper, we systematically compare the performance of five popular ConvNet architectures when applied to three different image classification problems in astronomy to determine which architecture works best for each problem. We show that a VGG-style architecture pre-trained on ImageNet yields the best results on all studied problems, even when compared to architectures which perform much better on the ImageNet competition.

Paper Citation

in Harvard Style

Martinazzo A., Espadoto M. and Hirata N. (2020). Deep Learning for Astronomical Object Classification: A Case Study. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP; ISBN 978-989-758-402-2, SciTePress, pages 87-95. DOI: 10.5220/0008939800870095

in Bibtex Style

@conference{visapp20,
author={Ana Martinazzo and Mateus Espadoto and Nina S. T. Hirata},
title={Deep Learning for Astronomical Object Classification: A Case Study},
booktitle={Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP},
year={2020},
pages={87-95},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008939800870095},
isbn={978-989-758-402-2},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2020) - Volume 5: VISAPP
TI - Deep Learning for Astronomical Object Classification: A Case Study
SN - 978-989-758-402-2
AU - Martinazzo A.
AU - Espadoto M.
AU - Hirata N.
PY - 2020
SP - 87
EP - 95
DO - 10.5220/0008939800870095
PB - SciTePress