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dc.contributor.authorLiu, Chuntian
dc.contributor.authorWei, Wei
dc.contributor.authorBai, Xiao
dc.contributor.authorZhou, Jun
dc.contributor.editorClive S. Fraser, Jeff Walker, Mark L. Williams
dc.date.accessioned2017-05-03T16:11:58Z
dc.date.available2017-05-03T16:11:58Z
dc.date.issued2013
dc.date.modified2014-04-22T05:05:34Z
dc.identifier.isbn9781479911141
dc.identifier.issn2153-6996
dc.identifier.urihttp://hdl.handle.net/10072/58739
dc.description.abstractMany image features can be extracted from very high resolution remote sensing images for object classification. Proper feature combination is a step towards better classification performance. In this paper, we propose a logistic regressionbased feature fusion method which assigns different weights to different features. This method considers the probability that two images belongs to the same classes and the imageto- class similarity to define the similarity between two objects. This similarity is used as a marginalized kernel for the final classifier construction. Experiments on remote sensing images suggest that this approach is effective in various feature combination, and has outperformed the SVM baseline method.
dc.description.publicationstatusYes
dc.format.extent563097 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
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.ispartofpagefrom216
dc.relation.ispartofpageto219
dc.rights.retentionY
dc.subject.fieldofresearchComputer Vision
dc.subject.fieldofresearchcode080104
dc.titleMarginalized kernel-based feature fusion method for VHR object classification
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


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