Remote Sensing Applications and Decision Support

Support vector data description model to map urban extent from National Polar-Orbiting Partnership Satellite–Visible Infrared Imaging Radiometer Suite nightlights and normalized difference vegetation index

[+] Author Affiliations
Jinshui Zhang

Beijing Normal University, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing 100875, China

Beijing Normal University, College of Resources Science and Technology, Beijing 100875, China

Michigan State University, Department of Geological Sciences, East Lansing, Michigan 48824, United States

Zhongwei Zhou, Guanyuan Shuai, Hongli Liu

Beijing Normal University, State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing 100875, China

Beijing Normal University, College of Resources Science and Technology, Beijing 100875, China

J. Appl. Remote Sens. 10(2), 026012 (May 02, 2016). doi:10.1117/1.JRS.10.026012
History: Received November 24, 2015; Accepted March 25, 2016
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Abstract.  We explored a one-class classifier, the support vector data description (SVDD), using the Suomi National Polar-Orbiting Partnership Satellite–Visible Infrared Imaging Radiometer Suite and normalized difference vegetation index to map the urban extent, which was tested in the Beijing and Tianjin city group area. The urban edge-pixels were selected as training samples for SVDD based on a profile-based sampling method combining nighttime light value histograms. The results showed that the overall accuracy of SVDD was similar to the support vector machine (SVM) model. However, kappa coefficients of SVDD for highly developed cities were superior to SVM, as producer and user accuracies of SVDD were almost equal to show high agreement of urban and nonurban areas. For metropolitan areas, such as Beijing and Tianjin, the urban extent generated by SVDD is closer to the reference data. The R2 between the quantity of SVDD-estimated urban extent and population, 0.86, was higher than that obtained from SVM, 0.76, indicating that the estimated urban extent from the SVDD is more efficient for understanding the population development. The SVDD was further applied for three other representative metropolitans in China: Shanghai, Guangzhou, and Shenzhen to validate the SVDD’s performance, and similar results were achieved. The success of the SVDD-based urban extent extraction improves our ability to map urban extent at regional and national scales.

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© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Jinshui Zhang ; Zhongwei Zhou ; Guanyuan Shuai and Hongli Liu
"Support vector data description model to map urban extent from National Polar-Orbiting Partnership Satellite–Visible Infrared Imaging Radiometer Suite nightlights and normalized difference vegetation index", J. Appl. Remote Sens. 10(2), 026012 (May 02, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.026012


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