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Article: Automatic land-cover update approach integrating iterative training sample selection and a markov random field model

TitleAutomatic land-cover update approach integrating iterative training sample selection and a markov random field model
Authors
Issue Date2014
Citation
Remote Sensing Letters, 2014, v. 5, n. 2, p. 148-156 How to Cite?
AbstractLand-cover updating from remote-sensing data is an effective means of obtaining timely land-cover information. An automatic approach integrating iterative training sample selection (ITSS) and a Markov Random Field (MRF) model is proposed in this study to overcome the land-cover update problem when no previous remote-sensing data corresponding to the land-cover data are available. A case study in the Beijing region indicates that ITSS can effectively select reliable training samples based on historical land-cover data and that ITSS with MRF can achieve satisfactory land-cover update results (overall classification accuracy: 83.1%). The MRF model can effectively reduce salt-and-pepper noise and improve overall accuracy by approximately 6%. The proposed approach is completely unsupervised and has no strict requirements for newly acquired remote-sensing data for land-cover updating. © 2014 Taylor & Francis.
Persistent Identifierhttp://hdl.handle.net/10722/321569
ISSN
2023 Impact Factor: 1.4
2023 SCImago Journal Rankings: 0.458
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorJia, Kun-
dc.contributor.authorLiang, Shunlin-
dc.contributor.authorWei, Xiangqin-
dc.contributor.authorZhang, Lei-
dc.contributor.authorYao, Yunjun-
dc.contributor.authorGao, Shuai-
dc.date.accessioned2022-11-03T02:19:52Z-
dc.date.available2022-11-03T02:19:52Z-
dc.date.issued2014-
dc.identifier.citationRemote Sensing Letters, 2014, v. 5, n. 2, p. 148-156-
dc.identifier.issn2150-704X-
dc.identifier.urihttp://hdl.handle.net/10722/321569-
dc.description.abstractLand-cover updating from remote-sensing data is an effective means of obtaining timely land-cover information. An automatic approach integrating iterative training sample selection (ITSS) and a Markov Random Field (MRF) model is proposed in this study to overcome the land-cover update problem when no previous remote-sensing data corresponding to the land-cover data are available. A case study in the Beijing region indicates that ITSS can effectively select reliable training samples based on historical land-cover data and that ITSS with MRF can achieve satisfactory land-cover update results (overall classification accuracy: 83.1%). The MRF model can effectively reduce salt-and-pepper noise and improve overall accuracy by approximately 6%. The proposed approach is completely unsupervised and has no strict requirements for newly acquired remote-sensing data for land-cover updating. © 2014 Taylor & Francis.-
dc.languageeng-
dc.relation.ispartofRemote Sensing Letters-
dc.titleAutomatic land-cover update approach integrating iterative training sample selection and a markov random field model-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1080/2150704X.2014.889862-
dc.identifier.scopuseid_2-s2.0-84896357172-
dc.identifier.volume5-
dc.identifier.issue2-
dc.identifier.spage148-
dc.identifier.epage156-
dc.identifier.eissn2150-7058-
dc.identifier.isiWOS:000333935200006-

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