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Open AccessArticle

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

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School of Computer Science and Engineering, Xi’An University of Technology, Xi’an 710048, China
2
Key Laboratory of Geospatial Big Data Mining and Application, Changsha 410081, China
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Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik IS 107, Iceland
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School of Electronical and Information Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China
5
College of Resources and Environmental Sciences, Hunan Normal University, Changsha 410081, China
*
Authors to whom correspondence should be addressed.
Remote Sens. 2018, 10(11), 1809; https://doi.org/10.3390/rs10111809
Received: 20 September 2018 / Revised: 29 October 2018 / Accepted: 31 October 2018 / Published: 15 November 2018
(This article belongs to the Special Issue Change Detection Using Multi-Source Remotely Sensed Imagery)
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. View Full-Text
Keywords: land use and land cover; remote sensing application; detection algorithm; histogram distance land use and land cover; remote sensing application; detection algorithm; histogram distance
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MDPI and ACS Style

Lv, Z.; Liu, T.; Atli Benediktsson, J.; Lei, T.; Wan, Y. Multi-Scale Object Histogram Distance for LCCD Using Bi-Temporal Very-High-Resolution Remote Sensing Images. Remote Sens. 2018, 10, 1809.

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