Title: | Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images |
Author: |
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Date: | 2018-11-15 |
Language: | English |
Scope: | 1809 |
University/Institute: | Háskóli Íslands University of Iceland |
School: | Verkfræði- og náttúruvísindasvið (HÍ) School of Engineering and Natural Sciences (UI) |
Department: | Rafmagns- og tölvuverkfræðideild (HÍ) Faculty of Electrical and Computer Engineering (UI) |
Series: | Remote Sensing;10(11) |
ISSN: | 2072-4292 |
DOI: | 10.3390/rs10111809 |
Subject: | Land use and land cover; Remote sensing application; Detection algorithm; Histogram distance; Landnýting; Fjarkönnun |
URI: | https://hdl.handle.net/20.500.11815/1276 |
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
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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.
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Description:Publisher's version (útgefin grein)
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Rights: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
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