Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/66802
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dc.contributorDepartment of Land Surveying and Geo-Informatics-
dc.creatorShao, P-
dc.creatorShi, WZ-
dc.creatorHe, PF-
dc.creatorHao, M-
dc.creatorZhang, XK-
dc.date.accessioned2017-05-22T02:26:48Z-
dc.date.available2017-05-22T02:26:48Z-
dc.identifier.urihttp://hdl.handle.net/10397/66802-
dc.language.isoenen_US
dc.publisherMolecular Diversity Preservation International (MDPI)en_US
dc.rights© 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/).en_US
dc.rightsThe following publication Shao, P., Shi, W. Z., He, P. F., Hao, M., & Zhang, X. K. (2016). Novel approach to unsupervised change detection based on a robust semi-supervised FCM clustering algorithm. Remote Sensing, 8(3), (Suppl. ), - is available athttps://dx.doi.org/10.3390/rs8030264en_US
dc.subjectRobust semi-supervised fuzzy C-meansen_US
dc.subjectThresholdingen_US
dc.subjectRemote sensingen_US
dc.subjectClustering with partial supervisionen_US
dc.subjectUnsupervised change detectionen_US
dc.subjectFuzzy C-meansen_US
dc.titleNovel approach to unsupervised change detection based on a robust semi-supervised FCM clustering algorithmen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.volume8-
dc.identifier.issue3-
dc.identifier.doi10.3390/rs8030264-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationRemote sensing, Mar. 2016, v. 8, no. 3, p. 1-25-
dcterms.isPartOfRemote sensing-
dcterms.issued2016-
dc.identifier.isiWOS:000373627400014-
dc.identifier.eissn2072-4292-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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