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Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images

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dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor.author Lv, ZhiYong
dc.contributor.author Liu, TongFei
dc.contributor.author Benediktsson, Jon Atli
dc.contributor.author Lei, Tao
dc.contributor.author Wan, YiLiang
dc.date.accessioned 2019-10-01T13:19:55Z
dc.date.available 2019-10-01T13:19:55Z
dc.date.issued 2018-11-15
dc.identifier.citation Lv, Z., Liu, T., Atli Benediktsson, J., Lei, T., & Wan, Y. (2018). Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images. Remote Sensing, 10(11), 1809. doi:10.3390/rs10111809
dc.identifier.issn 2072-4292
dc.identifier.uri https://hdl.handle.net/20.500.11815/1276
dc.description Publisher's version (útgefin grein)
dc.description.abstract To improve the performance of land-cover change detection (LCCD) using remote sensing images, this study utilises spatial information in an adaptive and multi-scale manner. It proposes a novel multi-scale object histogram distance (MOHD) to measure the change magnitude between bi-temporal remote sensing images. Three major steps are related to the proposed MOHD. Firstly, multi-scale objects for the post-event image are extracted through a widely used algorithm called the fractional net evaluation approach. The pixels within a segmental object are taken to construct the pairwise frequency distribution histograms. An arithmetic frequency-mean feature is then defined from the red, green and blue band histogram. Secondly, bin-to-bin distance is adapted to measure the change magnitude between the pairwise objects of bi-temporal images. The change magnitude image (CMI) of the bi-temporal images can be generated through object-by-object. Finally, the classical binary method Otsu is used to divide the CMI to a binary change detection map. Experimental results based on two real datasets with different land-cover change scenes demonstrate the effectiveness of the proposed MOHD approach in detecting land-cover change compared with three widely used existing approaches.
dc.description.sponsorship This work was supported by the National Science Foundation China (61701396), Open Fund of Key Laboratory of Geospatial Big Data Mining and Application, Hunan Province (No. 201802), the Natural Science Foundation of Shaan Xi Province (2017JQ4006), and Xizang Minzu University Youth Training program-Study on urban morphology expansion in Lhasa.
dc.format.extent 1809
dc.language.iso en
dc.publisher MDPI AG
dc.relation.ispartofseries Remote Sensing;10(11)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Land use and land cover
dc.subject Remote sensing application
dc.subject Detection algorithm
dc.subject Histogram distance
dc.subject Landnýting
dc.subject Fjarkönnun
dc.title Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images
dc.type info:eu-repo/semantics/article
dcterms.license This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
dc.description.version Peer Reviewed
dc.identifier.journal Remote Sensing
dc.identifier.doi 10.3390/rs10111809
dc.relation.url http://www.mdpi.com/2072-4292/10/11/1809/pdf
dc.contributor.department Rafmagns- og tölvuverkfræðideild (HÍ)
dc.contributor.department Faculty of Electrical and Computer Engineering (UI)
dc.contributor.school Verkfræði- og náttúruvísindasvið (HÍ)
dc.contributor.school School of Engineering and Natural Sciences (UI)

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