Show simple item record

dc.contributor.authorGuo, Zhouxiao
dc.contributor.authorYang, Haichuan
dc.contributor.authorBai, Xiao
dc.contributor.authorZhang, Zhihong
dc.contributor.authorZhou, Jun
dc.contributor.editorFraser, Clive S.
dc.contributor.editorWalker, Jeff
dc.contributor.editorWilliams, Mark L.
dc.date.accessioned2017-05-03T16:11:57Z
dc.date.available2017-05-03T16:11:57Z
dc.date.issued2013
dc.date.modified2014-04-22T05:32:59Z
dc.identifier.isbn9781479911141
dc.identifier.issn2153-6996
dc.identifier.urihttp://hdl.handle.net/10072/58773
dc.description.abstractBand selection is an important step towards effective and efficient object classification in hyperspectral imagery. In this paper, we propose a semi-supervised learning method for band selection based on a sparse linear regression model. This model uses a least absolute shrinkage and selection operator to compute the regression coefficients from both labeled and unlabeled samples. These coefficients are then used to compute a contribution score for each band, which allows bands with high scores being selected for the testing step. During this process, unlabeled samples also contribute to the coefficients calculation. In order to propagate the labels to these samples, a hypergraph is first built to describe the relationship between labeled and unlabeled samples. This leads to an adjacency matrix whose entries are the sum of corresponding weights of hyperedges. Then matrix subspace learning method is used to estimate the labels of unlabeled samples. The proposed method is evaluated on the APHI dataset. Comparison with several baseline methods has shown the advantages of the proposed method on the pixel-level classification.
dc.description.publicationstatusYes
dc.format.extent276396 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.publisherIEEE
dc.publisher.placeUnited States
dc.publisher.urihttp://www.igarss2013.org/
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofconferencenameIEEE International Geoscience and Remote Sensing Symposium (IGARSS)
dc.relation.ispartofconferencetitle2013 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM (IGARSS)
dc.relation.ispartofdatefrom2013-07-21
dc.relation.ispartofdateto2013-07-26
dc.relation.ispartoflocationMelbourne, AUSTRALIA
dc.relation.ispartofpagefrom1474
dc.relation.ispartofpageto1477
dc.rights.retentionY
dc.subject.fieldofresearchImage Processing
dc.subject.fieldofresearchcode080106
dc.titleSemi-supervised hyperspectral band selection via sparse linear regression and hypergraph models
dc.typeConference output
dc.type.descriptionE2 - Conferences (Non Refereed)
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


Files in this item

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record