An Unsupervised Classification Method for Hyperspectral Image Combining PCA and Gaussian Mixture Model

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 5286, Issue 2, 2003, Pages 729-734

  Wu, Hao ^a   Kuang, Gangyao ^a   Yu, Wenxian ^a  

^a NATIONAL UNIVERSITY OF DEFENSE TECHNOLOGY   (China)

Author keywords

Dimensionality reduction; Expectation Maximization (EM) algorithm; Gaussian Mixture Model; Hyperspectral data analysis; Principal Component Analysis (PCA); Unsupervised Classification

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); COMPUTATIONAL METHODS; CONVERGENCE OF NUMERICAL METHODS; DATA REDUCTION; MATHEMATICAL MODELS; PARAMETER ESTIMATION; PRINCIPAL COMPONENT ANALYSIS; VECTORS;

DIMENSIONALITY REDUCTION; EXPECTATION-MAXIMIZATION (EM) ALGORITHM; GAUSSIAN MIXTURE MODELS; HYPERSPECTRAL DATA ANALYSIS; UNSUPERVISED CLASSIFICATION;

IMAGE ANALYSIS;

EID: 1242330411     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.539872     Document Type: Conference Paper

References (7)

1. D. Landgrebe, "Information Extraction Principles and Methods for Multispectral and Hyperspectral Image Data," Chapter 1 of Information Processing for Remote Sensing, edited by C. H. Chen, published by the World Scientific Publishing Co., Inc., 1060 Main Street, River Edge, NJ 07661, USA January 2000.
2. S. Roweis, "EM Algorithms for PCA and SPCA," Advances in Neural Information Processing Systems, 10, pp. 626-632, 1998.
3. A. K. Jain and R. Dubes, Algorithms for Clustering Data, Prentice Hall, Englewood Cliffs, N. J., 1988.
4. A. P. Dempster, N. M. Laird and D. B. Rubin. "Maximum-likelihood from incomplete data via the EM algorithm," J. Royal Stat. Soc. Ser. B, 39, pp. 1-38, 1977.
5. R. A. Redner and H. F. Walker, "Mixture density, maximum likelihood and the EM algorithm," SIAM Review, 26(2), pp. 195-239, 1984.
6. G. Schwarz, "Estimating the dimension of a model," The Anneals of Statistics, 6, pp. 461-464, 1978.
7. C. Fraley and A. E. Raftery, "How many clusters? Which clustering method? Answers via model-based cluster analysis", Technical Report, http://www.stat.washington.edu/www/research/reports/1998/tr329.ps, 1998.