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


Publisher's version (útgefin grein)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.This work was supported by the National Science Foundation China (61701396), Open Fund of Key Laboratory of Geospatial Big Data Mining and Application, Hunan Province (No. 201802), the Natural Science Foundation of Shaan Xi Province (2017JQ4006), and Xizang Minzu University Youth Training program-Study on urban morphology expansion in Lhasa.Peer Reviewe

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oaioai:opinvisindi.is:20.500.11815/1276Last time updated on 10/29/2019View original full text link

This paper was published in Opin visindi.

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