Stratified spectral mixture analysis of medium resolution imagery for impervious surface mapping
Sun, Genyun and Chen, Xiaolin and Ren, Jinchang and Zhang, Aizhu and Jia, Xiuping (2017) Stratified spectral mixture analysis of medium resolution imagery for impervious surface mapping. International Journal of Applied Earth Observations and Geoinformation. ISSN 0303-2434 (In Press)
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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.
ORCID iDs
Sun, Genyun, Chen, Xiaolin, Ren, Jinchang
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Item type: Article ID code: 60499 Dates: DateEvent12 April 2017Published12 April 2017AcceptedKeywords: impervious surface, stratification, spectral mixture analysis, CBI, combinational build-up index, normalized difference vegetaion index, prior-knowledge-based spectral mixture analysis, Physical geography, Earth and Planetary Sciences(all) Subjects: Geography. Anthropology. Recreation > Physical geography Department: Faculty of Engineering > Electronic and Electrical Engineering
Technology and Innovation Centre > Sensors and Asset ManagementDepositing user: Pure Administrator Date deposited: 24 Apr 2017 09:28 Last modified: 25 May 2023 09:57 Related URLs: URI: https://strathprints.strath.ac.uk/id/eprint/60499