Galaxy classification with deep convolutional neural networks

Shi, Honghui

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https://hdl.handle.net/2142/90939 Copy

Description

Title

Galaxy classification with deep convolutional neural networks

Author(s)

Shi, Honghui

Issue Date

2016-04-26

Director of Research (if dissertation) or Advisor (if thesis)

Huang, Thomas S.

Department of Study

Electrical & Computer Eng

Discipline

Electrical & Computer Engr

Degree Granting Institution

University of Illinois at Urbana-Champaign

Degree Name

M.S.

Degree Level

Thesis

Keyword(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.

Graduation Semester

2016-05

Type of Resource

text

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

Copyright and License Information

Copyright 2016 Honghui Shi

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