Morphological Classification of Galaxies with Different Grayscale Images Using Deep Learning

  • João Pedro Holanda Universidade Federal do Piauí
  • Melissa Sales Universidade Federal do Piauí
  • Marcus Ferraz Universidade Federal do Piauí
  • Roney Santos Universidade Federal do Piauí


In recent years, several images of galaxies have been collected by telescopes, so that they could be morphologically analyzed using artificial intelligence devices. Thus, this work aims to analyze new image processing approaches, using grayscale conversion algorithms, in order to explore their influence on CNN (Convolutional Neural Networks). Images from the Galaxies10 DECals dataset of two different types of galaxies were used, which were grayscaled and analyzed separately on the CNN. Aspects such as the influence of redshift and the average pixel value have been studied, since grayscale conversions depend on the influence of each channel. It was concluded that, besides altering the assertiveness of the CNN, the applied grayscales also facilitate recognition by the CNN in specific cases.

Palavras-chave: Image Processing, Grayscale, Galaxies, Convolutional Neural Networks


Abazajian, K. N., Adelman-McCarthy, J. K., Agüeros, M. A., Allam, S. S., Prieto, C. A., An, D., Anderson, K. S., Anderson, S. F., Annis, J., Bahcall, N. A., et al. (2009). The seventh data release of the sloan digital sky survey. The Astrophysical Journal Supplement Series, 182(2):543.

Abbott, T., Abdalla, F., Allam, S., Amara, A., Annis, J., Asorey, J., Avila, S., Ballester, O., Banerji, M., Barkhouse, W., et al. (2018). The dark energy survey: Data release 1. The Astrophysical Journal Supplement Series, 239(2):18.

Brandão, A. S., Pizziolo, T. A. O., Souza, R. N. O., and Faria, M. N. (2005). Redes neurais artificiais aplicadas ao reconhecimento de comandos de voz. Trabalho de Conclusão de Curso. Engenharia Elétrica, Universidade Federal de Viçosa–UFV–2005.

Cardoso, N. C., Schwarz, G. O., Dias, L. D., Bom, C. B., Sodré Jr, L., and Oliveira, C. M. (2021). Classificação morfológica de galáxias no s-plus por combinação de redes convolucionais. NOTAS TÉCNICAS, 11(2).

Chaisson, E., McMillan, S., and Rice, E. (2005). Astronomy today. Pearson/Prentice Hall Upper Saddle River, NJ.

Domínguez Sánchez, H., Huertas-Company, M., Bernardi, M., Tuccillo, D., and Fischer, J. (2018). Improving galaxy morphologies for sdss with deep learning. Monthly Notices of the Royal Astronomical Society, 476(3):3661–3676.

Eisenstein, D. J., Weinberg, D. H., Agol, E., Aihara, H., Prieto, C. A., Anderson, S. F., Arns, J. A., Aubourg, É., Bailey, S., Balbinot, E., et al. (2011). Sdss-iii: Massive spectroscopic surveys of the distant universe, the milky way, and extra-solar planetary systems. The Astronomical Journal, 142(3):72.

Elmegreen, D. M. (1998). Galaxies and galactic structure. New Jersey: Prentice Hall.

Gray, R. and Dunning-Davies, J. (2008). A review of redshift and its interpretation in cosmology and astrophysics. arXiv preprint arXiv:0806.4085.

Hubble, E. (1929). A relation between distance and radial velocity among extra-galactic nebulae. Proceedings of the national academy of sciences, 15(3):168–173.

Hubble, E. P. (1926). Extragalactic nebulae. Astrophysical Journal, 64, 321-369 (1926), 64.

Kanan, C. and Cottrell, G. W. (2012). Color-to-grayscale: does the method matter in image recognition? PloS one, 7(1):e29740.

Ketkar, N. (2017). Introduction to keras. Deep learning with python: a hands-on introduction, pages 97–111.

LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P. (1998). Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324.

Mitchell, H. B. (1976). Henrietta swan leavitt and cepheid variables. The Physics Teacher, 14(3):162–167.

Oliveira Filho, K. d. S. and Saraiva, M. d. F. O. (2004). Astronomia e astrofısica. Rio Grande do Sul: Livraria da Fısica.

Perez, L. and Wang, J. (2017). The effectiveness of data augmentation in image classification using deep learning. arXiv preprint arXiv:1712.04621.

Reber, G. (1995). Intergalactic plasma. Astrophysics and Space Science, 227:93–96.

Silva, M. and Ventura, T. (2019). Classificaçao morfológica de galáxias por meio de redes neurais. In Anais da X Escola Regional de Informática de Mato Grosso, pages 31–36. SBC.

Vázquez-Mata, J. A., Hernandez-Toledo, H. M., and Mascherpa, L. C. (2020). Galaxy Morphology classification using CNN. In Proceedings of Artificial Intelligence for Science, Industry and Society — PoS(AISIS2019), volume 372, page 006.

Walmsley, M., Lintott, C., Géron, T., Kruk, S., Krawczyk, C., Willett, K. W., Bamford, S., Kelvin, L. S., Fortson, L., Gal, Y., et al. (2022). Galaxy zoo decals: Detailed visual morphology measurements from volunteers and deep learning for 314 000 galaxies. Monthly Notices of the Royal Astronomical Society, 509(3):3966–3988.

Wang, J., Perez, L., et al. (2017). The effectiveness of data augmentation in image classification using deep learning. Convolutional Neural Networks Vis. Recognit, 11(2017):1– 8.

Wright, T. (2014). An Original Theory or New Hypothesis of the Universe, Founded upon the Laws of Nature. Cambridge University Press.

Wu, J. (2017). Introduction to convolutional neural networks. National Key Lab for Novel Software Technology. Nanjing University. China, 5(23):495.

Yamauchi, C., Ichikawa, S.-i., Doi, M., Yasuda, N., Yagi, M., Fukugita, M., Okamura, S., Nakamura, O., Sekiguchi, M., and Goto, T. (2005). Morphological classification of galaxies using photometric parameters: The concentration index versus the coarseness parameter. The Astronomical Journal, 130(4):1545.

Zhu, X.-P., Dai, J.-M., Bian, C.-J., Chen, Y., Chen, S., and Hu, C. (2019). Galaxy morphology classification with deep convolutional neural networks. Astrophysics and Space Science, 364:1–15.
HOLANDA, João Pedro; SALES, Melissa; FERRAZ, Marcus; SANTOS, Roney. Morphological Classification of Galaxies with Different Grayscale Images Using Deep Learning. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 20. , 2023, Belo Horizonte/MG. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2023 . p. 909-923. ISSN 2763-9061. DOI: