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Automatic approach to solve the morphological galaxy classification problem using the sparse representation technique and dictionary learning

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Abstract

The observation of celestial objects in the sky is a practice that helps astronomers to understand the way in which the Universe is structured. However, due to the large number of observed objects with modern telescopes, the analysis of these by hand is a difficult task. An important part in galaxy research is the morphological structure classification based on the Hubble sequence. In this research, we present an approach to solve the morphological galaxy classification problem in an automatic way by using the Sparse Representation technique and dictionary learning with K-SVD. For the tests in this work, we use a database of galaxies extracted from the Principal Galaxy Catalog (PGC) and the APM Equatorial Catalogue of Galaxies obtaining a total of 2403 useful galaxies. In order to represent each galaxy frame, we propose to calculate a set of 20 features such as Hu’s invariant moments, galaxy nucleus eccentricity, gabor galaxy ratio and some other features commonly used in galaxy classification. A stage of feature relevance analysis was performed using Relief-f in order to determine which are the best parameters for the classification tests using 2, 3, 4, 5, 6 and 7 galaxy classes making signal vectors of different length values with the most important features. For the classification task, we use a 20-random cross-validation technique to evaluate classification accuracy with all signal sets achieving a score of 82.27 % for 2 galaxy classes and up to 44.27 % for 7 galaxy classes.

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Acknowledgments

The authors want to thank to the Consejo Nacional de Ciencia y Tecnología (CONACyT, México) for the support through the postdoctoral residency to develop this work. The author A.E. Ortiz-Esquivel thanks CONACyT for the financial support through project CB-2011-01-169755 and support provided by the master’s degree scholarship with registration number 627528. We thank Saula Tecpanecatl Mani (Plates Laboratory, INAOE, México) for her hard work and invaluable experience, and to Carlos Torres (IPN, México) for his help in data analysis. We acknowledge the use of NASA’s SkyView facility (http://skyview.gsfc.nasa.gov) located at NASA Goddard Space Flight Center.

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Authors and Affiliations

  1. National Institute of Astrophysics, Optics and Electronics, Luis Enrique Erro #1, Santa María Tonantzintla, San Andres Cholula, Puebla, CP, 72840, Mexico

    R. Diaz-Hernandez, A. Ortiz-Esquivel, H. Peregrina-Barreto, L. Altamirano-Robles & J. Gonzalez-Bernal

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  1. R. Diaz-Hernandez
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  2. A. Ortiz-Esquivel
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  3. H. Peregrina-Barreto
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  4. L. Altamirano-Robles
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  5. J. Gonzalez-Bernal
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Correspondence to H. Peregrina-Barreto.

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Diaz-Hernandez, R., Ortiz-Esquivel, A., Peregrina-Barreto, H. et al. Automatic approach to solve the morphological galaxy classification problem using the sparse representation technique and dictionary learning. Exp Astron 41, 409–426 (2016). https://doi.org/10.1007/s10686-016-9495-0

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