Xception-AS: An Automatic Object Classification Algorithm Based on the Structure of Xception Model

In this paper, an algorithm based on Xception is proposed, which can be used to solve the problem of automatic classification of galaxies, stars and quasars. Based on Xception, the algorithm is improved by selecting the optimal activation function and adding attention mechanism. In this paper, 11 543 galaxies, 10 490 quasars and 11 967 stars in SDSS-DR16 photometric image data are randomly selected as experimental data from 34 000 observation sources in g, r and i bands, and multiple experiments are designed to verify and test the algorithm. A comprehensive analysis of all experimental results shows that the algorithm in this paper achieves 90.26%, 90.01%, 89.86% and 89.85% respectively in the key indicators such as precision rate, accuracy rate, recall rate and F₁ score. Compared with other 12 classical and popular convolutional neural network algorithms on the same data set, the proposed Xception-AS algorithm has better classification performance, which proves that the proposed algorithm has advantages in solving the problem of automatic classification of celestial objects.