Optimisation of Gaussian mixture model for satellite image classification

IEE Proceedings: Vision, Image and Signal Processing

Volumn 153, Issue 3, 2006, Pages 349-356

  Zhou, X ^a   Wang, X ^b  

^a TEXAS A AND M UNIVERSITY   (United States)

^b Columbia University ^*  (United States)

Author keywords

[No Author keywords available]

Indexed keywords

FEATURE EXTRACTION; GENETIC ALGORITHMS; IMAGE COMMUNICATION SYSTEMS; OPTIMIZATION; PATTERN RECOGNITION; REMOTE SENSING; SATELLITE COMMUNICATION SYSTEMS; VECTORS;

BAYESIAN APPROACH; FEATURE VECTORS; GAUSSIAN MIXTURE MODEL; PATTERN LEARNING;

IMAGE SEGMENTATION;

EID: 33646678467     PISSN: 1350245X     EISSN: None     Source Type: Journal    
DOI: 10.1049/ip-vis:20045126     Document Type: Article

References (35)

1. Augustteijin, M.F., Clemens, L.E., and Shaw, K.A.: ' Performance evaluation of texture measures for ground cover identification in satellite images by means of a neural network classifier ', IEEE Trans. Geosci. Remote Sens., 2000, 38, (2), p. 1044-1063
2. Wilkinson, G.G.: ' Results and implications of a study of fifteen years of satellite image classification experiments ', IEEE Trans. Geosci. Remote Sens., 2005, 43, (3), p. 433-440
3. Ohanian, P.P., and Dubes, R.C.: ' Performance evaluation for four classes of textural features ', Pattern Recognit., 1992, 25, (8), p. 819-833
4. Paola, J.D., and Schowengerdt, R.A.: ' A detailed comparison of backpropagation neural network and maximum-likelihood classifiers for urban land use classification ', IEEE Trans. Geosci. Remote Sens., 1995, 33, (4), p. 981-996
5. Lobo, A.: ' Image segmentation and discriminant analysis for the identification of land cover units in ecology ', IEEE Trans. Geosci. Remote Sens., 1997, 35, (5), p. 1136-1145
6. Chai, B.B., Huang, T., Zhuang, X., Zhao, Y., and Sklansky, J.: ' Piecewise linear classifiers using binary tree structure and genetic algorithm ', Pattern Recognit., 1996, 29, (11), p. 1905-1917
7. Li, H., and Abe, N.: ' Generalizing case frames using a thesaurus and the MDL principle ', Comput. Linguist., 1998, 24, (2), p. 217-244
8. Chien, J.T.: ' On-line hierarchical transformation of hidden Markov models for speech recognition ', IEEE Trans. Speech Audio Process., 1999, 7, (6), p. 656-667
9. Gauvain, J.L., and Lee, C.H.: ' Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains ', IEEE Trans. Speech Audio Process., 1994, 2, (2), p. 291-298
10. McLachlan, G., and Krishnan, T.: ' The EM algorithm and extensions ', (John Wiley & Sons, New York 1997)
11. Reynolds, D.A.: ' Speaker identification and verification using Gaussian mixture speaker models ', Speech Commun., 1995, 17, (1-2), p. 91-108
12. Frankot, R.T., and Chellappa, A.: ' Lognormal random-field models and their applications to radar image synthesis ', IEEE Trans. Geosci. Remote Sens., 1999, 25, (2), p. 195-207
13. Chang, C.I., and Ren, H.: ' An experiment-based quantitative and comparative analysis of target detection and image classification algorithms for hyperspectral imagery ', IEEE Trans. Geosci. Remote Sens., 2000, 38, (2), p. 1044-1063
14. Haralick, R., Shanmugam, K., and Dinstein, I.: ' Textural features for image classification ', IEEE Trans. Syst. Man Cybern., 1973, SMC-6, p. 610-621
15. Schistad Solberg, A.H., Taxt, T., and Jain, A.H.: ' A Markov random field model for classification of multisource satellite imagery ', IEEE Trans. Geosci. Remote Sens., 1996, 34, (1), p. 100-113
16. Smits, P.C., and Annoni, A.: ' Updating land-cover maps by using texture information from very high-resolution space-borne imagery ', IEEE Trans. Geosci. Remote Sens., 1999, 37, (3), p. 1244-1254
17. Smits, P.C., and Dellepiane, S.G.: ' Synthetic aperture radar image segmentation by detail observing Markov random field approach ', IEEE Trans. Geosci. Remote Sens., 1997, 35, (4), p. 844-857
18. Solaiman, B., Pierce, E., and Ulaby, F.T.: ' Multisensor data fusion using fuzzy concepts: application to land-cover classification using ERS-1/JERS-1 SAR composites ', IEEE Trans. Geosci. Remote Sens., 1999, 37, (3), p. 1316-1326
19. Weszka, J.S., Dyer, C.R., and Rosenfeld, A.: ' A comparative study of texture measures of terrain classification ', IEEE Trans. Syst. Man Cybern., 1976, SMC-4, p. 269-285
20. Makhoul, J., Roucos, S., and Gish, H.: ' Vector quantization in speech coding ', Proc. IEEE, 1985, 73, p. 1551-1588
21. Kohonen, T.: ' The self-organizing map ', Proc. IEEE, 1990, 78, (9), p. 1464-1480
22. ' Intertwining themes in the theories of model order estimation ', Research Report, University of Illinois at Urbana-Champaign, Coordinated Science Laboratory, 1991
23. Rissanen, J.: ' Stochastic complexity in statistical inquiry ', (World Scientific Publishing Company 1989)
24. Hung, Y.J., and Swain, P.H.: ' Bayesian contextual classification based on modified M-estimates and Markov random fields ', IEEE Trans. Geosci. Remote Sens., 1996, 34, (1), p. 67-78
25. Gales, M.J.: ' Semi-tied covariance matrixes for hidden Markov models ', IEEE Trans. Speech Audio Process., 1999, 7, (3), p. 272-281
26. Juang, B.H., and Katagiri, S.: ' Discriminative learning for minimum error classification ', IEEE Trans. Signal Process., 1992, 40, p. 3043-3054
27. Juang, B.H., Chou, W., and Lee, C.H.: ' Minimum classification error rate methods for speech recognition ', IEEE Trans. Speech Audio Process., 1997, 5, (3), p. 257-265
28. Davis, L.: ' Handbook of genetic algorithm ', Davis, L., (Van Nostrand Reinhold, New York 1991)
29. Goldberg, D.E.: ' Genetic algorithms in search, optimization and machine learning ', (Addison-Wesley, Reading, Massachusetts 1989)
30. Palaniappan, K., Zhu, F., Zhuang, X., Zhao, Y., and Blanchard, A.: ' Enhanced binary tree genetic algorithm for automatic cover classification ', IEEE Int. Geosci. Remote Sensing Symposium (IGARSS), Honolulu, HI p. 24-28
31. Zhu, Y., Zhao, Y., Palaniappan, K., Zhou, X., and Zhuang, X.: ' Optimal Bayesian classifier for land cover classification using Landsat TM data ', IEEE Int. Geosci. Remote Sensing Symposium (IGARSS), Honolulu, HI p. 447-450
32. Li, J., Najmi, A., and Gray, R.M.: ' Image classification by a two-dimensional hidden Markov model ', IEEE Trans. Signal Process., 2000, 48, (2), p. 517-533
33. Amari, S., and Wu, S.: ' Improving support vector machine classifiers by modifying kernel functions ', Neural Netw., 1999, 12, p. 783-789
34. Pontil, M., and Verri, A.: ' Support vector machines for 3D object recognition ', IEEE Trans. Pattern Anal. Mach. Intell., 1998, 20, (6), p. 637-646
35. Smola, A.J., Scholkopf, B., and Muller, K.R.: ' The connection between regularization operators and support vector kernels ', Neural Netw., 1998, 11, p. 637-649