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

ZhiYong Lv, TongFei Liu, Jon Atli Benediktsson, Tao Lei, YiLiang Wan

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)


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.
Original languageEnglish
Number of pages1809
JournalRemote Sensing
Issue number11
Publication statusPublished - 15 Nov 2018

Other keywords

  • Land use and land cover
  • Remote sensing application
  • Detection algorithm
  • Histogram distance
  • Landnýting
  • Fjarkönnun


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