Automatic Classification of Galaxies Based on SVM

Abstract

Viewing heavenly objects in the sky helps astronomers understand how the world is shaped. Regarding the large number of objects observed by modern telescopes, it is very difficult to manually analyze it manually. An important part of galactic research is classification based on Hubble's design. The purpose of this research is to classify images of the stars using machine learning and neural networks. Particularly in this study, the galaxy's image is employed. The galaxies are divided into regular two-dimensional Hubble designs and an irregular bunch. The regular bands that are presented in the shape of the Hubble design are divided into two distinct spiral and elliptical galaxies. Spiral galaxies can be considered as elliptical or circular galaxies depending on the shape of the spiral, so the identification or classification of the spiral galaxy is considered important from other galaxies. In the proposed algorithm, the Sloan Digital Sky is used for testing, including 570 images. In the first step, its preprocessing operation is performed to remove image noise. In the next step, extracting the attribute from the galactic images takes place in a total of 827 properties using the sub-windows, the moments of different color spaces and the properties of the local configuration patterns. Then the classification is performed after extracting the property using a Support vector machine. And then compared with other methods, which indicate that our approach has worked better. In this study, the experiments were carried out in two spiral and elliptic classes and three spiral, elliptic and zinc-edged classes with a precision of 96 and 94 respectively.