Galaxy classification with deep convolutional neural networks

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.

Name thesis:degree_name

M.S.

Level thesis:degree_level

Thesis

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2016

Author dc:creator

• Shi, Honghui

Contributors dc:contributor

• Huang, Thomas S.

• Galaxy Classification
• Convolutional Neural Networks

Statement dc:rights

• Copyright 2016 Honghui Shi

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/90939

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/90939