{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/90939"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/90939","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Galaxy classification with deep 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.","abstract_html":"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.","abstract_has_math":false,"creators":["Shi, Honghui"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Huang, Thomas S."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2016,"date_issued":"2016-07-07T21:17:52Z","date_published":"2016-07-07T21:17:52Z","updated_at":"2026-07-22T22:26:34Z","subjects":["Galaxy Classification","Convolutional Neural Networks"],"languages":["en"],"rights":["Copyright 2016 Honghui Shi"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/90939","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Huang, Thomas S."]},{"key":"dc:creator","label":"Author","values":["Shi, Honghui"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2016-07-07T21:17:52Z","2018-07-08T09:15:20Z","2016-04-26","2016-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Galaxy Classification","Convolutional Neural Networks"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2016 Honghui Shi"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/90939"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["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.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-05-01","The student, Honghui Shi, accepted the attached license on 2016-04-20 at 15:32.","The student, Honghui Shi, submitted this Thesis for approval on 2016-04-20 at 15:46.","This Thesis was approved for publication on 2016-04-26 at 09:28.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9386 on 2016-07-07 at 14:17:35","Made available in DSpace on 2016-07-07T21:17:52Z (GMT). No. of bitstreams: 2 SHI-THESIS-2016.pdf: 4254004 bytes, checksum: 09e485fb4f8169bab7e4c553a1af319b (MD5) LICENSE.txt: 4208 bytes, checksum: a3412add2c68bc23ddeee8497bb1ef01 (MD5) Previous issue date: 2016-04-26","Embargo set by: Seth Robbins for item 93294 Lift date: 2018-07-07T21:18:16Z Reason: Author requested closed access (OA after 2yrs) in Vireo ETD system","Limited Restriction Lifted for Item 93294 on 2018-07-08T09:15:20Z."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Galaxy classification with deep convolutional neural networks"]}]}],"canonical_facts":{"dc:contributor":["Huang, Thomas S."],"dc:creator":["Shi, Honghui"],"dc:date":["2016-07-07T21:17:52Z","2018-07-08T09:15:20Z","2016-04-26","2016-05"],"dc:description":["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.","Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2018-05-01","The student, Honghui Shi, accepted the attached license on 2016-04-20 at 15:32.","The student, Honghui Shi, submitted this Thesis for approval on 2016-04-20 at 15:46.","This Thesis was approved for publication on 2016-04-26 at 09:28.","DSpace SAF Submission Ingestion Package generated from Vireo submission #9386 on 2016-07-07 at 14:17:35","Made available in DSpace on 2016-07-07T21:17:52Z (GMT). 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