Deep Convolutional Neural Networks models for the binary morphological classification of SDSS-galaxies

Abstract

We present the deep learning approach for the determination of morphological types of galaxies. We demonstrate the method's performance with the redshift-limited (z < 0.1) training sample of 6 163 galaxies from the SDSS DR9. We exploited the deep convolutional neural network classifiers such as InceptionV3, DenseNet121, and MobileNetV2 to process images of SDSS-galaxies (100x100 pixels, 25 arcsec in each axis in size) using g, r, i filters as R - G - B channels to create images. We provided the data augmentation (horizontal and vertical flips, random shifts on ±10 pixels, and rotations) randomly applied to the set of images during learning, which helped increase the classifier's generalization ability. Also, two different loss functions, MAE and Lovasz-Softmax, were applied to each classifier. The target sample galaxies were classified into two morphological types (late and early) trained on the images of galaxies from the sample. It turned out that the deep convolutional neural networks InceptionV3 and DenseNet121 with MAE-loss function show the best result attaining 93.3% accuracy.