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dc.contributor.authorLi, Huapeng
dc.contributor.authorZhang, Shuqing
dc.contributor.authorDing, Xiaohui
dc.contributor.authorZhang, Ce
dc.contributor.authorDale, Patricia
dc.date.accessioned2017-08-31T12:30:41Z
dc.date.available2017-08-31T12:30:41Z
dc.date.issued2016
dc.identifier.issn2072-4292
dc.identifier.doi10.3390/rs8040295
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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherMDPI AG
dc.relation.ispartofpagefrom295-1
dc.relation.ispartofpageto295-22
dc.relation.ispartofissue4
dc.relation.ispartofjournalRemote Sensing
dc.relation.ispartofvolume8
dc.subject.fieldofresearchConceptual Modelling
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscience
dc.subject.fieldofresearchGeomatic Engineering
dc.subject.fieldofresearchClassical Physics
dc.subject.fieldofresearchcode080603
dc.subject.fieldofresearchcode0406
dc.subject.fieldofresearchcode0909
dc.subject.fieldofresearchcode0203
dc.titlePerformance Evaluation of Cluster Validity Indices (CVIs) on Multi/Hyperspectral Remote Sensing Datasets
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
dcterms.licensehttp://creativecommons.org/licenses/by/4.0/
dc.description.versionPublished
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.
gro.hasfulltextFull Text
gro.griffith.authorDale, Patricia E.


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