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Morphological Galaxies Classification According to Hubble-de Vaucouleurs Diagram Using CNNs

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Image Analysis and Processing – ICIAP 2022 (ICIAP 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13231))

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Abstract

Galaxies morphology classification is a crucial task for studying their physical properties, formation and evolutionary histories. The large-scale surveys on universe has boosted the need to develop techniques for automated galaxies morphological classification. This paper proposes a system able to classify automatically galaxies according to the Hubble De Vaucouleurs diagram. We introduce a novel CNN architectures that for the first time was trained to automatically classify galaxies according to 26-classes Hubble-De Vaucouleurs scheme. We use Galaxy Zoo dataset, using the decision tree, to extract a labeled examples containing an even amount of images of each 26-classes. We also compared different CNN Backbones in order to assess obtained galaxies classification results. We obtain a balanced multi-class accuracy (BCA) of more than 80% in classifying all 26 Hubble-De Vaucouleurs galaxy categories.

The authors thank Arturo Argentieri for his technical support in the setup of the hardware used for network training and data processing.

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Author information

Authors and Affiliations

  1. Universitá del Salento, Via Monteroni sn, 73100, Lecce, Italy

    Antonio Rizzo

  2. ISASI - CNR c/o DHITECH, Via Monteroni sn, 73100, Lecce, Italy

    Pier Luigi Mazzeo & Cosimo Distante

Authors
  1. Pier Luigi Mazzeo
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  2. Antonio Rizzo
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  3. Cosimo Distante
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Corresponding author

Correspondence to Pier Luigi Mazzeo .

Editor information

Editors and Affiliations

  1. Boston University, Boston, MA, USA

    Stan Sclaroff

  2. National Research Council, Lecce, Italy

    Cosimo Distante

  3. National Research Council, Lecce, Italy

    Marco Leo

  4. University of Catania, Catania, Italy

    Giovanni M. Farinella

  5. Technische Universität München, Garching, Germany

    Federico Tombari

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Mazzeo, P.L., Rizzo, A., Distante, C. (2022). Morphological Galaxies Classification According to Hubble-de Vaucouleurs Diagram Using CNNs. In: Sclaroff, S., Distante, C., Leo, M., Farinella, G.M., Tombari, F. (eds) Image Analysis and Processing – ICIAP 2022. ICIAP 2022. Lecture Notes in Computer Science, vol 13231. Springer, Cham. https://doi.org/10.1007/978-3-031-06427-2_5

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  • DOI: https://doi.org/10.1007/978-3-031-06427-2_5

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