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Title:Galaxy classification with deep convolutional neural networks
Author(s):Shi, Honghui
Department / Program:Electrical & Computer Eng
Discipline:Electrical & Computer Engr
Degree Granting Institution:University of Illinois at Urbana-Champaign
Subject(s):Galaxy Classification
Convolutional Neural Networks
Abstract:Galaxy classification, using digital images captured from sky surveys to determine the galaxy morphological classes, is of great interest to astronomy researchers. Conventional methods rely heavily on a few handcrafted morphological features while popular feature extraction methods that developed for natural images are not suitable for galaxy images. Deep convolutional neural networks (CNNs) are able to learn powerful features from images by hierarchical convolutional and pooling operations. This work applies state-of-the-art deep CNN technologies to galaxy classification for both a regression task and multi-class classification tasks. We also implement and compare the performance with several different conventional machine learning algorithms for a classification sub-task. Our experiments show that convolutional neural networks are able to learn representative features automatically and achieve high performance, surpassing both human recognition and other machine learning methods.
Issue Date:2016-04-26
Rights Information:Copyright 2016 Honghui Shi
Date Available in IDEALS:2016-07-07
Date Deposited:2016-05

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