Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76540
PIRA download icon_1.1View/Download Full Text
DC FieldValueLanguage
dc.contributorDepartment of Land Surveying and Geo-Informaticsen_US
dc.creatorZhang, Xen_US
dc.creatorShi, Wen_US
dc.creatorHao, Men_US
dc.creatorShao, Pen_US
dc.creatorLyu, Xen_US
dc.date.accessioned2018-05-10T02:56:09Z-
dc.date.available2018-05-10T02:56:09Z-
dc.identifier.issn2279-7254en_US
dc.identifier.urihttp://hdl.handle.net/10397/76540-
dc.language.isoenen_US
dc.publisherAssociazione Italiana di Telerilevamentoen_US
dc.rights© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group. This is an Open Access article distributed under the terms of the Creative Commons Attribution 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
dc.rightsThe following publication Zhang, X., Shi, W., Hao, M., Shao, P., & Lyu, X. (2017). Level set incorporated with an improved MRF model for unsupervised change detection for satellite images. European Journal of Remote Sensing, 50(1), 202-210.is available at https://doi.org/10.1080/22797254.2017.1308236en_US
dc.subjectLevel seten_US
dc.subjectMarkov random fielden_US
dc.subjectChange detectionen_US
dc.subjectSatellite imagesen_US
dc.titleLevel set incorporated with an improved MRF model for unsupervised change detection for satellite imagesen_US
dc.typeJournal/Magazine Articleen_US
dc.identifier.spage202en_US
dc.identifier.epage210en_US
dc.identifier.volume50en_US
dc.identifier.issue1en_US
dc.identifier.doi10.1080/22797254.2017.1308236en_US
dcterms.abstractThis study proposes the use of a level set incorporated with an improved Markov random field (MRF) model in unsupervised change detection for satellite images. MRF provides a means of modelling the spatial contextual information in the level set, and an edge indicator function is introduced into the MRF model to control the contribution of local information in the boundary areas to change detection. On the basis of the improved MRF model, local label relationships and edge information are considered in the level set energy functional to conduct a novel local term and attract the contours into desired objects. By merging the novel energy term, the proposed approach not only reduces noise but also obtains accurate outlines of the changed regions. Experimental results obtained with Landsat 7 Enhanced Thematic Mapper Plus and SPOT 5 data sets confirm the superiority of the proposed model when compared with state-of-the-art change detection methods.en_US
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationEuropean journal of remote sensing, 2017, v. 50, no. 1, p. 202-210en_US
dcterms.isPartOfEuropean journal of remote sensingen_US
dcterms.issued2017-
dc.identifier.isiWOS:000405204300017-
dc.identifier.eissn1129-8596en_US
dc.description.validate201805 bcrcen_US
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberLSGI-0484-
dc.description.fundingSourceOthersen_US
dc.description.fundingTextNational Natural Science Foundation of China [41331175]en_US
dc.description.pubStatusPublisheden_US
dc.identifier.OPUS28991258-
Appears in Collections:Journal/Magazine Article
Files in This Item:
File Description SizeFormat 
Zhang_Level_Set_Incorporated.pdf1.87 MBAdobe PDFView/Open
Open Access Information
Status open access
File Version Version of Record
Access
View full-text via PolyU eLinks SFX Query
Show simple item record

Page views

109
Last Week
0
Last month
Citations as of Mar 24, 2024

Downloads

19
Citations as of Mar 24, 2024

SCOPUSTM   
Citations

6
Citations as of Mar 29, 2024

WEB OF SCIENCETM
Citations

5
Last Week
0
Last month
Citations as of Mar 28, 2024

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.