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Volume 19 Issue 4
Aug 2008
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Article Contents
Yong GE, He-xiang BAI, Qiu-ming CHENG. Solution of Multiple-Point Statistics to Extracting Information from Remotely Sensed Imagery. Journal of Earth Science, 2008, 19(4): 421-428.
Citation: Yong GE, He-xiang BAI, Qiu-ming CHENG. Solution of Multiple-Point Statistics to Extracting Information from Remotely Sensed Imagery. Journal of Earth Science, 2008, 19(4): 421-428.

Solution of Multiple-Point Statistics to Extracting Information from Remotely Sensed Imagery

Funds:

the National Natural Science Foundation of China 40671136

the National High Technology Research and Development Program of China 2006AA06Z115

the National High Technology Research and Development Program of China 2006AA120106

More Information
  • Corresponding author: GE Yong, gey@lreis.ac.cn
  • Received Date: 28 Mar 2008
  • Accepted Date: 14 May 2008
  • Two phenomena of similar objects with different spectra and different objects with similar spectrum often result in the difficulty of separation and identification of all types of geographical objects only using spectral information. Therefore, there is a need to incorporate spatial structural and spatial association properties of the surfaces of objects into image processing to improve the accuracy of classification of remotely sensed imagery. In the current article, a new method is proposed on the basis of the principle of multiple-point statistics for combining spectral information and spatial information for image classification. The method was validated by applying to a case study on road extraction based on Landsat TM taken over the Chinese Yellow River delta on August 8, 1999. The classification results have shown that this new method provides overall better results than the traditional methods such as maximum likelihood classifier (MLC).

     

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