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dc.contributor.authorGuo, Zhouxiao
dc.contributor.authorBai, Xiao
dc.contributor.authorZhang, Zhihong
dc.contributor.authorZhou, Jun
dc.contributor.editorDavid Taubman and Min Wu
dc.date.accessioned2017-05-03T16:11:55Z
dc.date.available2017-05-03T16:11:55Z
dc.date.issued2013
dc.date.modified2014-03-24T04:05:26Z
dc.identifier.isbn9781479923410
dc.identifier.issn1522-4880
dc.identifier.doi10.1109/ICIP.2013.6738646
dc.identifier.urihttp://hdl.handle.net/10072/57162
dc.description.abstractBand selection is a fundamental problem in hyperspectral data processing. In this paper, we present a semi-supervised learning approach and a hypergraph model to select useful bands based on few labeled object information. The contributions of this paper are two-fold. Firstly, the hypergraph model captures multiple relationships between hyperspectral image samples. Secondly, the semi-supervised learning method not only utilizes unlabeled samples in the learning process to improve model performance, but also requires little labeled samples which can significantly reduce large amount of human labor and costs. The proposed approach is evaluated on AVIRIS and APHI datasets, which demonstrate its advantages over several other band selection methods.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent362378 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.publisherIEEE
dc.publisher.placeUnited States
dc.relation.ispartofstudentpublicationY
dc.relation.ispartofconferencename20th IEEE International Conference on Image Processing (ICIP)
dc.relation.ispartofconferencetitle2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013)
dc.relation.ispartofdatefrom2013-09-15
dc.relation.ispartofdateto2013-09-18
dc.relation.ispartoflocationMelbourne, AUSTRALIA
dc.relation.ispartofpagefrom3137
dc.relation.ispartofpageto3141
dc.rights.retentionY
dc.subject.fieldofresearchComputer vision
dc.subject.fieldofresearchcode460304
dc.titleA hypergraph based semi-supervised band selection method for hyperspectral image classification
dc.typeConference output
dc.type.descriptionE1 - Conferences
dc.type.codeE - Conference Publications
gro.rights.copyright© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
gro.date.issued2013
gro.hasfulltextFull Text
gro.griffith.authorZhou, Jun


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