Stratified spectral mixture analysis of medium resolution imagery for impervious surface mapping |
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| Institution: | 1. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, China;2. Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China;3. State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan, China;4. Department of Geosciences, University of Oslo, PO Box 1047, Blindern, N-0316 Oslo, Norway;1. School of Geosciences, China University of Petroleum (East China), Qingdao Shandong, 266580, China;2. Satellite Environment Center (SEC), Ministry of Environmental Protection (MEP) of China, Beijing, 100094, China;3. Institute of Petroleum Engineering (IPE), Heriot-watt University, Edinburgh Scotland, EH14 2AD, United Kingdom;1. School of Geosciences, China University of Petroleum (East China), Qingdao, Shandong 266580, China;2. School of Engineering and Information Technology, The University of New South Wales at Canberra, Canberra ACT2600, Australia;3. School of Computer Science and Information Technology, Royal Melbourne Institute of Technology University, Melbourne VIC3001, Australia |
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| Abstract: | 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. |
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| Keywords: | Impervious surface Stratification Spectral mixture analysis CBI |
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