NDLI: Improving the support vector machine-based method to map urban land of China using DMSP/OLS and SPOT VGT data

Content Provider	IEEE Xplore Digital Library
Author	Yang Yang Chun-yang He Shi-qiang Du
Copyright Year	2011
Description	Author affiliation: State Key Laboratory of Earth Surface Processes and Resource Ecology (Beijing Normal University), China, 100875 (Yang Yang; Chun-yang He; Shi-qiang Du)
Abstract	Extracting urban land of China timely and accurately is essential for recognizing and understanding the urban pattern and urbanization process in China. Stable nighttime light data obtained by the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) provides an economical and straightforward way to map the distribution of urban land. However, all the current methods of extracting urban land from DMSP/OLS data are difficult to effectively apply in the whole of China due to their inapplicability in large area with obvious regional variation. To address this problem, we proposed a stratified support vector machine-based method (SSVM). The urban land of China in 2008 extracted from DMSP/OLS and SPOT VGT NDVI data using SSVM showed that SSVM could extract urban land more effectively than the original support vector machine-based method (OSVM) in the nation where imbalance in economic development and regional variation were extremely obvious. The correlation coefficients between statistical data and urban land derived using SSVM (R>0.90, p<0.0001) were almost twice as much as those from OSVM (R<0.48, p<0.05). Meanwhile, the accuracy assessment using the Landsat ETM+ data with higher resolution also showed that SSVM effectively decreased the omission error and commission error of OSVM. The overall accuracy and Kappa of SSVM achieved 0.90 and 0.69, which were 0.09 and 0.17 higher than those of OSVM, respectively.
Starting Page	2141
Ending Page	2144
File Size	696726
Page Count	4
File Format	PDF
ISBN	9781457710032
ISSN	21537003
e-ISBN	9781457710056
DOI	10.1109/IGARSS.2011.6049589
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-24
Publisher Place	Canada
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Remote sensing Support vector machines Data mining Cities and towns Accuracy Satellites China urban land support vector machine stratification DMSP/OLS SPOT NDVI
Content Type	Text
Resource Type	Article

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