Combining parametric and non-parametric algorithms for a partially unsupervised classification of multitemporal remote-sensing images

Information Fusion

Volumn 3, Issue 4, 2002, Pages 289-297

  Bruzzone, Lorenzo ^a   Cossu, Roberto ^a   Vernazza, Gianni ^a  

^a UNIVERSITY OF TRENTO   (Italy)

Author keywords

Expectation maximization algorithm; Maximum likelihood classifier; Multiple classifier system; Radial basis function neural network; Unsupervised retraining algorithm

Indexed keywords

ALGORITHMS; CLASSIFIERS; IMAGE PROCESSING; MAXIMUM LIKELIHOOD ESTIMATION; PARAMETER ESTIMATION; RADIAL BASIS FUNCTION NETWORKS; REMOTE SENSING; SENSOR DATA FUSION;

MULTITEMPORAL REMOTE-SENSING IMAGES;

CLASSIFICATION (OF INFORMATION);

EID: 0036896357     PISSN: 15662535     EISSN: None     Source Type: Journal    
DOI: 10.1016/S1566-2535(02)00091-X     Document Type: Article

References (18)

1. Richards J.A. Remote Sensing Digital Image Analysis. second ed. 1993;Springer-Verlag, New York.
2. Benediktsson J.A., Swain P.H., Ersoy O.K. Neural networks approaches versus statistical methods in classification of multisource remote sensing data. IEEE Transactions on Geoscience and Remote Sensing. 28:1990;540-552.
3. Benediktsson J.A., Swain P.H. Consensus theoretic classification methods. IEEE Transactions on Systems, Man and Cybernetics. 22:1992;688-704.
4. Bruzzone L., Fernàndez Prieto D., Serpico S.B. A neural statistical approach to multitemporal and multisource remote-sensing image classification. IEEE Transactions on Geoscience and Remote Sensing. 37:1999;1350-1359.
5. Bruzzone L., Fernàndez Prieto D. A technique for the selection of kernel-function parameters in RBF neural networks for classification of remote-sensing images. IEEE Transactions on Geoscience and Remote-Sensing. 37:1999;1179-1184.
6. Bruzzone L., Fernández Prieto D. Unsupervised retraining of a maximum-likelihood classifier for the analysis of multitemporal remote-sensing images. IEEE Transactions on Geoscience and Remote Sensing. 39:2001;456-460.
7. Maselli F., Gilabert M.A., Conese C. Integration of high and low resolution NDVI data for monitoring vegetation in mediterranean environments. Remote Sensing of Environment. 63:1998;208-218.
8. Grignetti A., Salvatori R., Casacchia R., Manes F. Mediterranean vegetation analysis by multi-temporal satellite sensor data. International Journal of Remote Sensing. 18:1997;1307-1318.
9. Friedl M.A., Brodley C.E., Strahler A.H. Maximizing land cover accuracies produced by decision trees at continental to global scales. IEEE Transactions on Geoscience and Remote-Sensing. 37:1999;969-977.
10. Tou J.T., Gonzalez R.C. Pattern Recognition Principles. 1974;Addison, Reading, MA.
11. Dempster A.P., Laird N.M., Rubin D.B. Maximum likelihood from incomplete data via the EM algorithm. Journal of Royal Statistical Society. 39:1977;1-38.
12. Shahshahani B.M., Landgrebe D. The effect of unlabeled samples in reducing the small sample size problem and mitigating the Hughes phenomenon. IEEE Transactions on Geoscience and Remote-Sensing. 32:1994;1087-1095.
13. Moon T.K. The expectation-maximization algorithm. Signal Processing Magazine. 13:1996;47-60.
14. Miller D.J., Hasan S.U. Combined learning and use for a mixture model equivalent to the RBF classifier. Neural Computation. 10:1998;281-293.
15. Lam L., Suen C.Y. Application of majority voting to pattern recognition: An analysis of its behavior and performance. IEEE Transactions on System, man and Cybernetics. 27:1997;553-568.
16. Kittler J., Hatef M., Duin R.P.W., Mates J. On combining classifiers. IEEE Transactions on pattern Analysis and Machine Intelligence. 20:1998;126-239.
17. Haralick R.M., Shanmugan K., Dinstein I. Textural features for image classification. IEEE Transactions on System, Man and Cybernetics. 3:1973;610-621.
18. Bishop C. Neural Networks for Pattern Recognition. 1995;Clarendon Press, Oxford.