Artificial neural network classification of Karst rocky desertification degree using SPOT satellite imagery and DEM data

Lin Meng; Baoqing Hu; Lianglin Wu

doi:10.1117/12.901954

23 November 2011 Artificial neural network classification of Karst rocky desertification degree using SPOT satellite imagery and DEM data

Lin Meng, Baoqing Hu, Lianglin Wu

Author Affiliations +

Proceedings Volume 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications; 80060X (2011) https://doi.org/10.1117/12.901954
Event: Seventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2011), 2011, Guilin, China

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.

Citation Download Citation

Lin Meng, Baoqing Hu, and Lianglin Wu "Artificial neural network classification of Karst rocky desertification degree using SPOT satellite imagery and DEM data", Proc. SPIE 8006, MIPPR 2011: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 80060X (23 November 2011); https://doi.org/10.1117/12.901954

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