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Proceedings Paper

Artificial neural network classification of Karst rocky desertification degree using SPOT satellite imagery and DEM data
Author(s): Lin Meng; Baoqing Hu; Lianglin Wu
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Paper Abstract

Karst rocky desertification is a significant environmental and ecological problem in Southwest China. In this paper, the spectral information, spatial context and topography information were utilized to synthetically discriminate the Karst rocky desertification degree, which are derived from The SPOT satellite imagery and DEM. By the back-propagation neural network, we proposed the classification model structure and classified the rocky desertification levels in Du'an County of Guangxi province, China. The results verified the classification model of Karst rocky desertification degree is efficient and accurate.

Paper Details

Date Published: 23 November 2011
PDF: 5 pages
Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 80060X (23 November 2011); doi: 10.1117/12.901954
Show Author Affiliations
Lin Meng, Guangxi Teachers Education Univ. (China)
Baoqing Hu, Guangxi Teachers Education Univ. (China)
Lianglin Wu, Guangxi Teachers Education Univ. (China)


Published in SPIE Proceedings Vol. 8006:
MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Faxiong Zhang; Faxiong Zhang, Editor(s)

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