| Technology and Methodology |
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Extraction of urban impervious surface information from TM image |
| LI Weina1,2, YANG Jiansheng3, LI Xiao1,2, ZHANG Jilong2,4, LI Shiwei1,2 |
1. School of Information and Communication Engineering, North University of China, Taiyuan 030051, China;
2. Opto-Electronic Information and Instrument Engineering Technology Research Center of Shanxi Province, North University of China, Taiyuan 030051, China;
3. Department of Geography Ball State University Muncie, IN 47036, USA;
4. Key Laboratory of Instrumentation Science & Dynamic Measurement of Ministry of Education, North University of China, Taiyuan 030051, China |
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Abstract Based on analyzing the theory of the Optimum Band Combination, Principal Component Analysis (PCA) and NDISI, this paper presents an improved method, i.e., "experimental layer stack", to extract impervious surface of Taiyuan city, Shanxi Province, from Landsat TM image. Both unsupervised and supervised classification methods were used to classify the original multi-band image, PCA image, NDISI and experimental band combination images. The accuracies of the classification were assessed using 256 sampling points randomly selected from Google Earth high resolution image of Taiyuan. By comparison and analysis, the authors found that the experimental B combination method obtained the highest overall accuracy of 87.72% with the Kappa coefficient of 0.85.
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| Keywords
snow cover
mixed pixel decomposition
linear mixture model
end-member selection
Tianshan Mountains
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Issue Date: 21 February 2013
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2013,
Vol. 25
