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Proceedings Paper

Hyperspectral remote sensing classification based on SVM with end-member extraction
Author(s): Xinlu Ma; Weidong Yan; Hui Bian; Bin Sun; Peizhong Wang
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Paper Abstract

In order to enhance the accuracy of hyperspectral remote sensing classification, a classification method based on SVM with end-member extraction is presented. Firstly, the end-members are extracted using pure pixel index approach, and then the ground target is identified based on the spectral feature fitting , followed by the spectral classification of the hyperspectral remote sensing images with the Support Vector Machines. The experiment results indicated that the validity and efficiency of our method are more accurately than the traditional SVM solutions which simply use the regions of interest selected from image as the training samples.

Paper Details

Date Published: 26 October 2013
PDF: 5 pages
Proc. SPIE 8921, MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications, 89210B (26 October 2013); doi: 10.1117/12.2031271
Show Author Affiliations
Xinlu Ma, Northwest Institute of Nuclear Technology (China)
Weidong Yan, Northwest Institute of Nuclear Technology (China)
Hui Bian, Northwest Institute of Nuclear Technology (China)
Bin Sun, Northwest Institute of Nuclear Technology (China)
Peizhong Wang, Northwest Institute of Nuclear Technology (China)


Published in SPIE Proceedings Vol. 8921:
MIPPR 2013: Remote Sensing Image Processing, Geographic Information Systems, and Other Applications
Jinwen Tian; Jie Ma, Editor(s)

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