DOI: 10.1016/j.jag.2017.04.006
Scopus记录号: 2-s2.0-85030848239
论文题名: Stratified spectral mixture analysis of medium resolution imagery for impervious surface mapping
作者: Sun G ; , Chen X ; , Ren J ; , Zhang A ; , Jia X
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2017
卷: 60 起始页码: 38
结束页码: 48
语种: 英语
英文关键词: CBI
; Impervious surface
; Spectral mixture analysis
; Stratification
Scopus关键词: estimation method
; image resolution
; mapping
; NDVI
; pixel
; spatial resolution
; stratification
英文摘要: Linear spectral mixture analysis (LSMA) is widely employed in impervious surface estimation, especially for estimating impervious surface abundance in medium spatial resolution images. However, it suffers from a difficulty in endmember selection due to within-class spectral variability and the variation in the number and the type of endmember classes contained from pixel to pixel, which may lead to over or under estimation of impervious surface. Stratification is considered as a promising process to address the problem. This paper presents a stratified spectral mixture analysis in spectral domain (Sp_SSMA) for impervious surface mapping. It categorizes the entire data into three groups based on the Combinational Build-up Index (CBI), the intensity component in the color space and the Normalized Difference Vegetation Index (NDVI) values. A suitable endmember model is developed for each group to accommodate the spectral variation from group to group. The unmixing into the associated subset (or full set) of endmembers in each group can make the unmixing adaptive to the types of endmember classes that each pixel actually contains. Results indicate that the Sp_SSMA method achieves a better performance than full-set-endmember SMA and prior-knowledge-based spectral mixture analysis (PKSMA) in terms of R, RMSE and SE. © 2017 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79980
Appears in Collections: 气候变化事实与影响
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作者单位: School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong, China; Laboratory for Marine Mineral Resources, Qingdao National Laboratory for Marine Science and Technology, Qingdao, Shandong, China; School of Engineering and Information Technology, The University of New South Wales at CanberraACT, Australia; Department of Electronic and Electrical Engineering, University of Strathclyde, Glasgow, United Kingdom
Recommended Citation:
Sun G,, Chen X,, Ren J,et al. Stratified spectral mixture analysis of medium resolution imagery for impervious surface mapping[J]. International Journal of Applied Earth Observation and Geoinformation,2017-01-01,60