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Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal 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 Zhang, Penglin
dc.contributor.author Benediktsson, Jon Atli
dc.contributor.author Chen, Yixiang
dc.date.accessioned 2018-08-09T10:59:42Z
dc.date.available 2018-08-09T10:59:42Z
dc.date.issued 2018-06-08
dc.identifier.citation Lv, Z.; Liu, T.; Zhang, P.; Atli Benediktsson, J.; Chen, Y. Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal Remote Sensing Images. Remote Sens. 2018, 10, 901. doi:10.3390/rs10060901
dc.identifier.issn 2072-4292
dc.identifier.uri https://hdl.handle.net/20.500.11815/758
dc.description.abstract Land cover change detection (LCCD) based on bi-temporal remote sensing images plays an important role in the inventory of land cover change. Due to the benefit of having spatial dependency properties within the image space while using remote sensing images for detecting land cover change, many contextual information-based change detection methods have been proposed in past decades. However, there is still a space for improvement in accuracies and usability of LCCD. In this paper, a LCCD method based on adaptive contextual information is proposed. First, an adaptive region is constructed by gradually detecting the spectral similarity surrounding a central pixel. Second, the Euclidean distance between pairwise extended regions is calculated to measure the change magnitude between the pairwise central pixels of bi-temporal images. All the bi-temporal images are scanned pixel by pixel so the change magnitude image (CMI) can be generated. Then, the Otsu or a manual threshold is employed to acquire the binary change detection map (BCDM). The detection accuracies of the proposed approach are investigated by three land cover change cases with Landsat bi-temporal remote sensing images and aerial images with very high spatial resolution (0.5 m/pixel). In comparison to several widely used change detection methods, the proposed approach can produce a land cover change inventory map with a competitive accuracy
dc.description.sponsorship This work was supported by the National Science Foundation China (61701396), the Natural Science Foundation of Shaan Xi Province (2017JQ4006), Engineering Research Center of Geospatial Information and Digital Technology, NASG (SIDT20171003), The National Key Research and Development Program of China(018YFF0215006), Natural Science Foundation of Jiangsu Province, China (BK20150835), and Tibet Natural Science Foundation-The study of Tibet crop condition monitoring based on crop growth model and multi-source remote sensing data (2016-ZR-15-18).
dc.format.extent 901
dc.language.iso en
dc.publisher MDPI AG
dc.relation.ispartofseries Remote Sensing;10(6)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Land cover change detection
dc.subject Adaptive contextual information
dc.subject Bi-temporal remote sensing images
dc.subject Fjarkönnun
dc.subject Loftmyndir
dc.title Land Cover Change Detection Based on Adaptive Contextual Information Using Bi-Temporal 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. (CC BY 4.0).
dc.description.version Peer Reviewed
dc.identifier.journal Remote Sensing
dc.identifier.doi 10.3390/rs10060901
dc.relation.url http://www.mdpi.com/2072-4292/10/6/901/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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