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dc.contributor.authorYan, Cheng
dc.contributor.authorBai, Xiao
dc.contributor.authorRen, Peng
dc.contributor.authorBai, Lu
dc.contributor.authorTang, Wenzhong
dc.contributor.authorZhou, Jun
dc.date.accessioned2017-05-22T05:33:25Z
dc.date.available2017-05-22T05:33:25Z
dc.date.issued2016
dc.identifier.issn1545-598X
dc.identifier.doi10.1109/LGRS.2016.2553699
dc.identifier.urihttp://hdl.handle.net/10072/100016
dc.description.abstractWe present a novel band weighting strategy that exploits multiple binary support vector machines (SVMs) to maximize interclass spectral distances for multiclass hyperspectral remote image classification. Specifically, we commence by training binary SVMs based on the original training samples. We then balance the bands of training samples by maximizing the modified classification scores for SVMs. This balance scheme enlarges the distances between individual training samples and the SVM hyperplane. For each class, we reformulate the binary SVM objective function based on the balanced training samples, resulting in a weighting vector that associates a weight to each spectral band for the class. For a testing sample, we weight it and then classify it by using the binary SVM, both with respect to every individual class. The classification result is obtained from the classifier with the greatest score. Experiments on two benchmark data sets show the effectiveness of the proposed strategy.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherIEEE
dc.relation.ispartofpagefrom922
dc.relation.ispartofpageto925
dc.relation.ispartofissue7
dc.relation.ispartofjournalIEEE Geoscience and Remote Sensing Letters
dc.relation.ispartofvolume13
dc.subject.fieldofresearchElectrical and Electronic Engineering not elsewhere classified
dc.subject.fieldofresearchArtificial Intelligence and Image Processing
dc.subject.fieldofresearchElectrical and Electronic Engineering
dc.subject.fieldofresearchGeomatic Engineering
dc.subject.fieldofresearchcode090699
dc.subject.fieldofresearchcode0801
dc.subject.fieldofresearchcode0906
dc.subject.fieldofresearchcode0909
dc.titleBand Weighting via Maximizing Interclass Distance for Hyperspectral Image Classification
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dc.description.versionPost-print
gro.rights.copyright© 2016 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.hasfulltextFull Text
gro.griffith.authorZhou, Jun


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