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Refining Land Cover Classification Maps Based on Dual-Adaptive Majority Voting Strategy for Very High Resolution Remote Sensing Images

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dc.contributor Háskóli Íslands
dc.contributor University of Iceland
dc.contributor.author Cui, Guoqing
dc.contributor.author Lv, Zhiyong
dc.contributor.author Li, Guangfei
dc.contributor.author Benediktsson, Jon Atli
dc.contributor.author Lu, Yudong
dc.date.accessioned 2019-10-03T11:04:54Z
dc.date.available 2019-10-03T11:04:54Z
dc.date.issued 2018-08-07
dc.identifier.citation Cui, G., Lv, Z., Li, G., Atli Benediktsson, J., & Lu, Y. (2018). Refining Land Cover Classification Maps Based on Dual-Adaptive Majority Voting Strategy for Very High Resolution Remote Sensing Images. 10(8), 1238. doi:10.3390/rs10081238
dc.identifier.issn 2072-4292
dc.identifier.uri https://hdl.handle.net/20.500.11815/1289
dc.description Publisher's version (útgefin grein)
dc.description.abstract Land cover classification that uses very high resolution (VHR) remote sensing images is a topic of considerable interest. Although many classification methods have been developed, the accuracy and usability of classification systems can still be improved. In this paper, a novel post-processing approach based on a dual-adaptive majority voting strategy (D-AMVS) is proposed to improve the performance of initial classification maps. D-AMVS defines a strategy for refining each label of a classified map that is obtained by different classification methods from the same original image, and fusing the different refined classification maps to generate a final classification result. The proposed D-AMVS contains three main blocks. (1) An adaptive region is generated by gradually extending the region around a central pixel based on two predefined parameters (T1 and T2) to utilize the spatial feature of ground targets in a VHR image. (2) For each classified map, the label of the central pixel is refined according to the majority voting rule within the adaptive region. This is defined as adaptive majority voting. Each initial classified map is refined in this manner pixel by pixel. (3) Finally, the refined classified maps are used to generate a final classification map, and the label of the central pixel in the final classification map is determined by applying AMV again. Each entire classified map is scanned and refined pixel by pixel based on the proposed D-AMVS. The accuracies of the proposed D-AMVS approach are investigated with two remote sensing images with high spatial resolutions of 1.0 m and 1.3 m. Compared with the classical majority voting method and a relatively new post-processing method called the general post-classification framework, the proposed D-AMVS can achieve a land cover classification map with less noise and higher classification accuracies
dc.description.sponsorship This research was funded by the National Natural Science Foundation of China (grant number 41630634), the National Natural Science Foundation of China (grant number 61701396), and the Natural Science Foundation of Shaan Xi Province (grant number 2017JQ4006).
dc.format.extent 1238
dc.language.iso en
dc.publisher MDPI AG
dc.relation.ispartofseries Remote Sensing;10(8)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Land cover classification
dc.subject Very high spatial resolution remote sensing image
dc.subject Adaptive majority vote
dc.subject Post-classification
dc.subject Fjarkönnun
dc.subject Landfræðileg gögn
dc.title Refining Land Cover Classification Maps Based on Dual-Adaptive Majority Voting Strategy for Very High Resolution Remote Sensing Images
dc.type info:eu-repo/semantics/article
dcterms.license This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
dc.description.version Peer Reviewed
dc.identifier.journal Remote Sensing
dc.identifier.doi 10.3390/rs10081238
dc.relation.url http://www.mdpi.com/2072-4292/10/8/1238/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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