A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data

International Journal of Image and Data Fusion

Volumn 1, Issue 4, 2010, Pages 293-307

  Ceamanos, Xavier ^a   Waske, Björn ^b   Benediktsson, Jón Atli ^c   Chanussot, Jocelyn ^d   Fauvel, Mathieu ^e   Sveinsson, Johannes R ^c  

^a CNRS   (France)

^b UNIVERSITY OF BONN   (Germany)

^c UNIVERSITY OF ICELAND   (Iceland)

^d GIPSA LAB   (France)

^e INRIA Grenoble   (France)

Author keywords

Classification of remote sensing data; Decision fusion; Hyperspectral data; Support vector machines

Indexed keywords

EID: 79958718997     PISSN: 19479832     EISSN: 19479824     Source Type: Journal    
DOI: 10.1080/19479832.2010.485935     Document Type: Article

References (25)

1. Benediktsson, J.A., Chanussot, J., and Fauvel, M., 2007. Multiple classifier systems in remote sensing: from basics to recent developments. In: M. Haindl, J. Kittler and F. Roli, eds. Multiple Classifier Systems. Heidelberg, Germany: Springer, 501-512.
2. Benediktsson, J.A. and Kanellopoulos, I., 1999. Classification of multisource and hyperspectral data based on decision fusion. IEEE Transactions on Geoscience and Remote Sensing, 37 (3), 1367-1377.
3. Banfield, R., et al., 2007. A comparison of decision tree ensemble creation techniques. IEEE Transactions on Pattern Analysis and Machine Intelligence, 29, 173-180.
4. Breiman, L., 1996. Bagging predictors. Machine Learning, 24 (2), 123-140.
5. Briem, G.J., Benediktsson, J.A., and Sveinsson, J.R., 2002. Multiple classifiers applied to multisource remote sensing data. IEEE Transactions on Geoscience and Remote Sensing, 40 (10), 2291-2299.
6. Camps-Valls, G. and Bruzzone, L., 2005. Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 43 (6), 1351-1362.
7. Chen, C.-C. and Lin, C.-J., 2001. LIBSVM: A library for support vector machines. Available from: http://www.csie.ntu.edu.tw/_cjlin/libsvm [Accessed 10 May 2010].
8. Dalponte, M., Bruzzone, L., and Gianelle, D., 2008. Fusion of hyperspectral and LIDAR remote sensing data for classification of complex forest areas. IEEE Transactions in Geoscience and Remote Sensing, 46 (5), 1416-1427.
9. Kittler, J., 1998. Combining classifiers: a theoretical framework. Pattern Analysis and Applications, 1, 18-27.
10. Koetz, B., et al., 2008. Multi-source land cover classification for forest fire management based on imaging spectrometry and LiDAR data. Forest Ecology and Management, 256 (3), 263-271.
11. Krogh, A. and Vedelsby, J., 1995. Neural network ensembles, cross validation, and active learning. In: G. Tesauro, D. Touretzky and T. Leen, eds. Advances in neural information processing systems, Vol. 7, Cambridge, UK: MIT Press, 231-238.
12. Ham, J., et al., 2005. Investigation of the random forest framework for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 43, 492-501.
13. Hansen, L. and Salamon, P., 1990. Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12, 993-1001.
14. Hughes, G.F., 1968. On the mean accuracy of statistical pattern recognizers. IEEE Transactions on Information Theory, IT-14, 55-63.
15. Melgani, F. and Bruzzone, L., 2004. Classification of hyperspectral remote sensing images with support vector machines. IEEE Transactions on Geoscience and Remote Sensing, 42 (8), 1778-1790.
16. Opitz, D. and Shavlik, J., 1996. Actively searching for an effective neural-network ensemble. Connection Science, 8 (3/4), 337-353.
17. Pal, M. and Mather, P.M., 2004. Assessment of the effectiveness of support vector machines for hyperspectral data. Future Generation Computer Systems, 20, 1215-1225.
18. Pal, M. and Mather, P.M., 2006. Some issues in the classification of DAIS hyperspectral data. International Journal of Remote Sensing, 27 (14), 2895-2916.
19. Roli, F., Giacinto, G., and Vernazza, G., 2001. Methods for designing multiple classifier systems. Proceedings of the second international workshop on multiple classifier systems. Cambridge, UK, 78-87.
20. Schapire, R., 1990. The strength of weak learnability. Machine Learning, 5 (2), 197-227.
21. Scholkopf, B. and Smola, A.J., 2002. Learning with kernels, support vector machines, regularization, optimization and beyond. Cambridge: MIT Press.
22. Tadjudin, S. and Landgrebe, D.A., 1999. Covariance estimation with limited training samples. IEEE Transactions on Geoscience and Remote Sensing, 37 (4), 2113-2118.
23. Vapnik, V.N., 1998. Statistical learning theory. New York: Wiley.
24. Waske, B. and Benediktsson, J.A., 2007. Fusion of support vector machines for classification of multisensor data. IEEE Transactions in Geoscience and Remote Sensing, 45, 3858-3866.
25. Waske, B. and Van der Linden, S., 2008. Classifying multilevel imagery from SAR and optical sensors by decision fusion. IEEE Transactions on Geoscience and Remote Sensing, 46 (5), 1457-1466.