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Proceedings Paper

Unsupervised classification method for hyperspectral image combining PCA and Gaussian mixture model
Author(s): Hao Wu; Gangyao Kuang; Wenxian Yu
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Paper Abstract

An unsupervised classification method combining Principal Component Analysis (PCA) and Gaussian Mixture Model for hyperspectral image is proposed in this paper. It is based on the property that lower dimensional linear projections of high dimensional data sets have the tendency to be Gaussian, or a combination of Gaussian distributions as the dimension increases. The spectral dimensionality of the data is first reduced by a PCA linear projection; then the transformed data is modeled by a Gaussian mixture models, the parameters of the model are estimated using the Expectation-Maximimization (EM) algorithm in merge operations and the number of components is automatically selected based on Bayesian Information Criterion (BIC); finally the data after PCA transform is classified according to the mixture model. Applying the method to Push-broom Hyperspectral Imager (PHI) data shows that the method is quite effective without any a prior information.

Paper Details

Date Published: 25 September 2003
PDF: 6 pages
Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); doi: 10.1117/12.539872
Show Author Affiliations
Hao Wu, National Univ. of Defense Technology (China)
Gangyao Kuang, National Univ. of Defense Technology (China)
Wenxian Yu, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 5286:
Third International Symposium on Multispectral Image Processing and Pattern Recognition
Hanqing Lu; Tianxu Zhang, Editor(s)

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