Opin vísindi

Post-Processing Approach for Refining Raw Land Cover Change Detection of 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 Wan, Yiliang
dc.contributor.author Benediktsson, Jon Atli
dc.contributor.author Zhang, Xiaokang
dc.date.accessioned 2018-08-10T15:02:33Z
dc.date.available 2018-08-10T15:02:33Z
dc.date.issued 2018-03-17
dc.identifier.citation Lv, Z.; Liu, T.; Wan, Y.; Benediktsson, J.A.; Zhang, X. Post-Processing Approach for Refining Raw Land Cover Change Detection of Very High-Resolution Remote Sensing Images. Remote Sens. 2018, 10, 472. doi:10.3390/rs10030472
dc.identifier.issn 2072-4292
dc.identifier.uri https://hdl.handle.net/20.500.11815/762
dc.description.abstract In recent decades, land cover change detection (LCCD) using very high-spatial resolution (VHR) remote sensing images has been a major research topic. However, VHR remote sensing images usually lead to a large amount of noises in spectra, thereby reducing the reliability of the detected results. To solve this problem, this study proposes an object-based expectation maximization (OBEM) post-processing approach for enhancing raw LCCD results. OBEM defines a refinement of the labeling in a detected map to enhance its raw detection accuracies. Current mainstream change detection (preprocessing) techniques concentrate on proposing a change magnitude measurement or considering image spatial features to obtain a change detection map. The proposed OBEM approach is a new solution to enhance change detection accuracy by refining the raw result. Post-processing approaches can achieve competitive accuracies to the preprocessing methods, but in a direct and succinct manner. The proposed OBEM post-processing method synthetically considers multi-scale segmentation and expectation maximum algorithms to refine the raw change detection result. Then, the influence of the scale of segmentation on the LCCD accuracy of the proposed OBEM is investigated. Four pairs of remote sensing images, one of two pairs (aerial image with 0.5 m/pixel resolution) which depict two landslide sites on Landtau Island, Hong Kong, China, are used in the experiments to evaluate the effectiveness of the proposed approach. In addition, the proposed approach is applied, and validated by two case studies, LCCD in Tianjin City China (SPOT-5 satellite image with 2.5 m/pixel resolution) and Mexico forest fire case (Landsat TM images with 30 m/pixel resolution), respectively. Quantitative evaluations show that the proposed OBEM post-processing approach can achieve better performance and higher accuracies than several commonly used preprocessing methods. To the best of the authors’ knowledge, this type of post-processing framework is first proposed here for the field of LCCD using VHR remote sensing images.
dc.description.sponsorship This work was supported by the National Science Foundation China (61701396 and D010701), the Science Foundation of Hunan Province (Grant No. 2016JJ6100), the Natural Science Foundation of Shaan Xi Province (2017JQ4006), and the project from the China Postdoctoral Science Foundation (2015M572658XB).
dc.format.extent 472
dc.language.iso en
dc.publisher MDPI AG
dc.relation.ispartofseries Remote Sensing;10(3)
dc.rights info:eu-repo/semantics/openAccess
dc.subject Land cover change detection
dc.subject Spatial resolution
dc.subject Remote sensing images
dc.subject Multi-scale segmentation
dc.subject Fjarkönnun
dc.subject Loftmyndir
dc.subject Myndvinnsla
dc.title Post-Processing Approach for Refining Raw Land Cover Change Detection of 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. (CC BY 4.0).
dc.description.version Peer Reviewed
dc.identifier.journal Remote Sensing
dc.identifier.doi 10.3390/rs10030472
dc.relation.url http://www.mdpi.com/2072-4292/10/3/472/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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