Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/17604
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dc.contributor.authorLei, T-
dc.contributor.authorXue, D-
dc.contributor.authorLv, Z-
dc.contributor.authorLi, S-
dc.contributor.authorZhang, Y-
dc.contributor.authorNandi, AK-
dc.date.accessioned2019-03-05T13:29:21Z-
dc.date.available2018-09-01-
dc.date.available2019-03-05T13:29:21Z-
dc.date.issued2018-09-
dc.identifier.citationRemote Sensing, 2018, 10 (9)en_US
dc.identifier.issnhttp://dx.doi.org/10.3390/rs10091381-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/17604-
dc.language.isoenen_US
dc.publisherMDPIen_US
dc.titleUnsupervised change detection using fast fuzzy clustering for landslide mapping from very high-resolution imagesen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.3390/rs10091381-
dc.relation.isPartOfRemote Sensing-
pubs.issue9-
pubs.publication-statusPublished-
pubs.volume10-
Appears in Collections:Dept of Electronic and Electrical Engineering Research Papers

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