Multiple classifiers applied to multisource remote sensing data

IEEE Transactions on Geoscience and Remote Sensing

Volumn 40, Issue 10, 2002, Pages 2291-2299

  Briem, Gunnar Jakob ^a   Benediktsson, Jon Atli ^a   Sveinsson, Johannes R ^a  

^a UNIVERSITY OF ICELAND   (Iceland)

Author keywords

Bagging; Boosting; Consensus theory; Multiple classifiers; Multisource remote sensing data

Indexed keywords

ALGORITHMS; COMPUTATIONAL METHODS; DECISION THEORY; GEOGRAPHIC INFORMATION SYSTEMS; MAXIMUM LIKELIHOOD ESTIMATION; NONLINEAR PROGRAMMING; REMOTE SENSING; SENSOR DATA FUSION; STATISTICAL METHODS;

BOOSTING ALGORITHM; BOOTSTRAP AGGREGATING ALGORITHM; CONSENSUS THEORY; MULTIPLE CLASSIFIER; MULTISOURCE REMOTE SENSING;

PATTERN RECOGNITION;

EID: 0036821351     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2002.802476     Document Type: Article

References (21)

1. T. Lee, J. A. Richards, and P. H. Swain, "Probabilistic and evidential approaches for multisource data analysis," IEEE Trans. Geosci. Remote Sensing, vol. GE-25, pp. 283-293, 1987.
2. J. A. Benediktsson, P. H. Swain, and O. K. Ersoy, "Conjugate-gradient neural networks in classification of multisource and very-high-dimensional data," Int. J. Remote Sens., vol. 14, pp. 2883-2903, 1993.
3. D. H. Wolpert, "Stacked generalization," Neural Networks, vol. 5 pp. 241-259, 1992.
4. K. Tumer and J. Ghosh, "Analysis of decision boundaries in linearly combined neural classifiers," Pattern Recognit., vol. 29, pp. 341-348, 1996.
5. L. K. Hansen and P. Salamon, "Neural network ensembles," IEEE Trans. Pattern Anal. Machine Intell., vol. 12, pp. 993-1001, Oct. 1990.
6. J. A. Benediktsson, J. R. Sveinsson, and P. H. Swain, "Hybrid consensus theoretic classification," IEEE Trans. Geosci. Remote Sensing, vol. 35, pp. 833-843, July 1997.
7. J. A. Benediktsson, J. R. Sveinsson, O. K. Ersov, and P. H. Swain, "Parallel consensual neural networks," IEEE Trans. Neural Networks, vol. 8, pp. 54-65, Jan. 1997.
8. T. K. Ho, "A theory of multiple classifier systems and its application to visual word recognition," Ph.D. dissertation, State Univ. New York, Buffalo, 1992.
9. N. J. Nilsson, Learning Machines. New York: McGraw-Hill, 1965.
10. R. E. Schapire, "A brief introduction to boosting," in Proc. 16th Int. Joint Conf. Artificial Intelligence, 1999.
11. Y. Freund and R. E. Schapire, "Experiments with a new boosting algorithm," in Proc. 13th Int. Conf. Machine Learning, 1996.
12. L. Breiman, "Bagging predictors," Univ. California, Dept. Stat., Berkeley, Tech. Rep. 421, 1994.
13. K. Tumer and J. Ghosh, "Linear and order statistics combiners for pattern classification," in Combining Artificial Neural Nets, 1999, pp. 127-162.
14. J. A. Benediktsson and P. H. Swain, "Consensus theoretic classification methods," IEEE Trans. Syst. Man Cybern., vol. 22, pp. 688-704, July/Aug. 1992.
15. R. Kohavi, "The power of decision tables," in Proc. Euro. Conf. Machine Learning, 1995.
16. I. H. Witten and E. Frank. Data Mining-Practical Machine Learning Tools With Java Implementations. San Francisco, CA: Morgan Kaufmann, 2000.
17. J. R. Quinlan, C4.5, Programs for Machine Learning. San Francisco, CA: Morgan Kaufmann, 1993.
18. R. C. Holte, "Very simple classification rules perform well on most commonly used datasets," Mach. Learn., vol. 11, pp. 63-91, 1993.
19. E. Parzen, "On estimation of a probability density function and mode," Ann. Math. Stat., vol. 33, pp. 1065-1076, 1962.
20. I. H. Witten et al. (2001) Weka 3.2-Machine Learning Software in Java. [Online]. Available: http://www.cs.waikato.ac.nz/~ml/weka/.
21. D. G. Goodenough, M. Goldberg, G. Plunkett, and J. Zelek, "The CCRS SAR/ MSS Anderson River dataset," IEEE Trans. Geosci. Remote Sensing, vol. GE-25, pp. 360-367, 1987.