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dc.contributor.authorLi, Huapengen_US
dc.contributor.authorZhang, Shuqingen_US
dc.contributor.authorDing, Xiaohuen_US
dc.contributor.authorCe, Zhangen_US
dc.contributor.authorDale, Patriciaen_US
dc.date.accessioned2017-08-31T12:30:41Z
dc.date.available2017-08-31T12:30:41Z
dc.date.issued2016en_US
dc.identifier.issn2072-4292en_US
dc.identifier.doi10.3390/rs8040295en_US
dc.identifier.urihttp://hdl.handle.net/10072/100986
dc.description.abstractThe number of clusters (i.e., the number of classes) for unsupervised classification has been recognized as an important part of remote sensing image clustering analysis. The number of classes is usually determined by cluster validity indices (CVIs). Although many CVIs have been proposed, few studies have compared and evaluated their effectiveness on remote sensing datasets. In this paper, the performance of 16 representative and commonly-used CVIs was comprehensively tested by applying the fuzzy c-means (FCM) algorithm to cluster nine types of remote sensing datasets, including multispectral (QuickBird, Landsat TM, Landsat ETM+, FLC1, and GaoFen-1) and hyperspectral datasets (Hyperion, HYDICE, ROSIS, and AVIRIS). The preliminary experimental results showed that most CVIs, including the commonly used DBI (Davies-Bouldin index) and XBI (Xie-Beni index), were not suitable for remote sensing images (especially for hyperspectral images) due to significant between-cluster overlaps; the only effective index for both multispectral and hyperspectral data sets was the WSJ index (WSJI). Such important conclusions can serve as a guideline for future remote sensing image clustering applications.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherMDPI AGen_US
dc.relation.ispartofpagefrom295-1en_US
dc.relation.ispartofpageto295-22en_US
dc.relation.ispartofissue4en_US
dc.relation.ispartofjournalRemote Sensingen_US
dc.relation.ispartofvolume8en_US
dc.subject.fieldofresearchConceptual Modellingen_US
dc.subject.fieldofresearchcode080603en_US
dc.titlePerformance Evaluation of Cluster Validity Indices (CVIs) on Multi/Hyperspectral Remote Sensing Datasetsen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Peer Reviewed (HERDC)en_US
dc.type.codeC - Journal Articlesen_US
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/en_US
dc.description.versionPublisheden_US
gro.rights.copyright© 2016 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons by Attribution (CC-BY) license (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
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