DOI: 10.1016/j.jag.2013.12.001
Scopus记录号: 2-s2.0-84897371803
论文题名: Prior-knowledge-based spectral mixture analysis for impervious surface mapping
作者: Zhang J ; , He C ; , Zhou Y ; , Zhu S ; , Shuai G
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2014
卷: 28, 期: 1 起始页码: 201
结束页码: 210
语种: 英语
英文关键词: Impervious surface
; Prior-knowledge
; Spectral mixture analysis
; V-I-S
Scopus关键词: error analysis
; image classification
; mapping
; performance assessment
; spectral analysis
; spectral resolution
; urban area
; vegetation index
英文摘要: In this study, we developed a prior-knowledge-based spectral mixture analysis (PKSMA) to map impervious surfaces by using endmembers derived separately for high- and low-density urban regions. First, an urban area was categorized into high- and low-density urban areas, using a multi-step classification method. Next, in high-density urban areas that were assumed to have only vegetation and impervious surfaces (ISs), the vegetation-impervious model (V-I) was used in a spectral mixture analysis (SMA) with three endmembers: vegetation, high albedo, and low albedo. In low-density urban areas, the vegetation-impervious-soil model (V-I-S) was used in an SMA analysis with four endmembers: high albedo, low albedo, soil, and vegetation. The fraction of IS with high and low albedo in each pixel was combined to produce the final IS map. The root mean-square error (RMSE) of the IS map produced using PKSMA was about 11.0%, compared to 14.52% only using four-endmember SMA. Particularly in high-density urban areas, PKSMA (RMSE = 6.47%) showed better performance than four-endmember (15.91%). The results indicate that PKSMA can improve IS mapping compared to traditional SMA by using appropriately selected endmembers and is particularly strong in high-density urban areas. © 2013 Elsevier B.V. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79734
Appears in Collections: 气候变化事实与影响
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作者单位: State Key Laboratory of Earth Surface Processes and Resource Ecology (ESPRE), Beijing Normal University, Beijing 100875, China; College of Resources Science and Technology, Beijing Normal University, Beijing 100875, China; Center for Human-Environment System Sustainability (CHESS), Beijing Normal University, Beijing 100875, China; Pacific Northwest National Laboratory, 5825 University Research Court, Suite 3500, College Park, MD 20740, United States
Recommended Citation:
Zhang J,, He C,, Zhou Y,et al. Prior-knowledge-based spectral mixture analysis for impervious surface mapping[J]. International Journal of Applied Earth Observation and Geoinformation,2014-01-01,28(1)