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

Remote Sensing

Jón Atli Benediktsson¹  YiLiang Wan²  ZhiYong Lv³  TongFei Liu³  Tao Lei⁴ 

[1] Faculty of Electrical and Computer Engineering, University of Iceland, Reykjavik IS 107, Iceland;Key Laboratory of Geospatial Big Data Mining and Application, Changsha 410081, China;School of Computer Science and Engineering, Xi’An University of Technology, Xi’an 710048, China;School of Electronical and Information Engineering, Shaanxi University of Science and Technology, Xi’an 710021, China;

关键词: land use and land cover;    remote sensing application;    detection algorithm;    histogram distance;   

DOI  :  10.3390/rs10111809

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown