Classification of multispectral remote sensing image using Kernel Principal Component Analysis and neural network

Jie Yu; Zhongshan Zhang; Hongxia Ke; Peihuang Guo

doi:10.1117/12.833212

30 October 2009 Classification of multispectral remote sensing image using Kernel Principal Component Analysis and neural network

Jie Yu, Zhongshan Zhang, Hongxia Ke, Peihuang Guo

Author Affiliations +

Proceedings Volume 7496, MIPPR 2009: Pattern Recognition and Computer Vision; 74961N (2009) https://doi.org/10.1117/12.833212
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

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.

Citation Download Citation

Jie Yu, Zhongshan Zhang, Hongxia Ke, and Peihuang Guo "Classification of multispectral remote sensing image using Kernel Principal Component Analysis and neural network", Proc. SPIE 7496, MIPPR 2009: Pattern Recognition and Computer Vision, 74961N (30 October 2009); https://doi.org/10.1117/12.833212

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