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Proceedings Paper

Classification of multispectral remote sensing image using Kernel Principal Component Analysis and neural network
Author(s): Jie Yu; Zhongshan Zhang; Hongxia Ke; Peihuang Guo
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Paper Abstract

A method combined Kernel Principal Component Analysis (KPCA) with BP neural network is proposed for multispectral remote sensing image classification in this paper. Firstly, the KPCA transformation including Gaussian KPCA and polynomial KPCA is carried out to get the former three uncorrelated bands containing most information of the TM images with seven bands. Secondly, BP neural network classification is executed using the three bands data after KPCA transformation. For testifying, both the classical PCA and the KPCA are applied to the multispectral Landsat TM data for feature extraction. The results demonstrate that the method proposed in this paper can improve the classification accuracy compared with that of principal component analysis (PCA) and BP neural network.

Paper Details

Date Published: 30 October 2009
PDF: 8 pages
Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961N (30 October 2009); doi: 10.1117/12.833212
Show Author Affiliations
Jie Yu, Wuhan Univ. (China)
Zhongshan Zhang, Wuhan Univ. (China)
Hongxia Ke, Wuhan Univ. (China)
Peihuang Guo, Wuhan Univ. (China)

Published in SPIE Proceedings Vol. 7496:
MIPPR 2009: Pattern Recognition and Computer Vision
Mingyue Ding; Bir Bhanu; Friedrich M. Wahl; Jonathan Roberts, Editor(s)

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