Háskóli ÍslandsUniversity of IcelandLv, ZhiYongLiu, TongFeiBenediktsson, Jon AtliLei, TaoWan, YiLiang2019-10-012019-10-012018-11-15Lv, 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/rs101118092072-4292https://hdl.handle.net/20.500.11815/1276Publisher'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.1809eninfo:eu-repo/semantics/openAccessLand use and land coverRemote sensing applicationDetection algorithmHistogram distanceLandnýtingFjarkönnuninfo:eu-repo/semantics/articleRemote Sensing10.3390/rs10111809