Subpixel Land Cover Mapping Using Multiscale Spatial Dependence

doi:10.1109/TGRS.2018.2808410

	Subpixel Land Cover Mapping Using Multiscale Spatial Dependence
	Chen, Yuehong 1; Ge, Yong 2; Chen, Yu 3; Jin, Yan 2; An, Ru 1
刊名	IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
	2018-09-01
卷号	56 期号:9 页码:5097-5106
关键词	Mixed object multiscale spatial dependence (MSD) remotely sensed imagery subpixel mapping (SPM)
ISSN号	0196-2892
DOI	10.1109/TGRS.2018.2808410
通讯作者	Chen, Yuehong(chenyh@lreis.ac.cn)
英文摘要	This paper proposes a new subpixel mapping (SPM) method based on multiscale spatial dependence (MSD). At the beginning, it adopts object-based and pixel-based soft classifications to generate the class proportions within each object and each pixel, respectively. Then, the object-scale spatial dependence of land cover classes is extracted from the class proportions of objects, and the combined spatial dependence at both pixel scale and subpixel scale is obtained from the class proportions of pixels. Furthermore, these spatial dependences are fused as the MSD for each subpixel. Last, a linear optimization model on each object is built to determine where the land cover classes spatially distribute within each mixed object at subpixel scales. Three experiments on two synthetic images and a real remote sensing image are carried out to evaluate the effectiveness of MSD. The experimental results show that MSD performed better than four existing SPM methods by generating less isolated classified pixels than those generated by three pixel-based SPM methods and more land cover local details than that generated by an object-based SPM method. Hence, MSD provides a valuable solution to producing land cover maps at subpixel scales.
资助项目	National Natural Science Foundation of China[41701376] ; National Natural Science Foundation of China[41725006] ; Natural Science Foundation of Jiangsu province[BK20170866] ; Key Program of Chinese Academy of Sciences[ZDRW-ZS-2016-6-3-4] ; Fundamental Research Funds for the Central Universities[2017B11714] ; China Postdoctoral Science Foundation[2016M600356] ; State Key Laboratory of Resources and Environmental Information System
WOS关键词	MARKOV-RANDOM-FIELD ; REMOTELY-SENSED IMAGERY ; SENSING IMAGERY ; NEURAL-NETWORK ; SUPERRESOLUTION ; ALGORITHM ; INFORMATION ; IDENTIFICATION ; CONSTRAINTS ; INUNDATION
WOS研究方向	Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology
语种	英语
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号	WOS:000443147600009
资助机构	National Natural Science Foundation of China ; Natural Science Foundation of Jiangsu province ; Key Program of Chinese Academy of Sciences ; Fundamental Research Funds for the Central Universities ; China Postdoctoral Science Foundation ; State Key Laboratory of Resources and Environmental Information System
内容类型	期刊论文
源URL	[http://ir.igsnrr.ac.cn/handle/311030/54372]
专题	中国科学院地理科学与资源研究所
通讯作者	Chen, Yuehong
作者单位	1.Hohai Univ, Sch Earth Sci & Engn, Nanjing 210098, Jiangsu, Peoples R China 2.Univ Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China 3.Nanjing Normal Univ, Sch Geog Sci, Nanjing 210023, Jiangsu, Peoples R China
推荐引用方式 GB/T 7714	Chen, Yuehong,Ge, Yong,Chen, Yu,et al. Subpixel Land Cover Mapping Using Multiscale Spatial Dependence[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2018,56(9):5097-5106.
APA	Chen, Yuehong,Ge, Yong,Chen, Yu,Jin, Yan,&An, Ru.(2018).Subpixel Land Cover Mapping Using Multiscale Spatial Dependence.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,56(9),5097-5106.
MLA	Chen, Yuehong,et al."Subpixel Land Cover Mapping Using Multiscale Spatial Dependence".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 56.9(2018):5097-5106.