A Multiple SVM System for Classification of Hyperspectral Remote Sensing Data

Journal of the Indian Society of Remote Sensing

Volumn 41, Issue 4, 2013, Pages 763-776

  Bigdeli, Behnaz ^a   Samadzadegan, Farhad ^a   Reinartz, Peter ^b  

^a UNIVERSITY OF TEHRAN   (Iran)

^b GERMAN AEROSPACE CENTER DLR   (Germany)

Author keywords

Bayesian Theory; Hyperspectral; Multiple Classifier System; Support Vector Machine

Indexed keywords

BAYESIAN ANALYSIS; DATA SET; IMAGE CLASSIFICATION; REMOTE SENSING; SATELLITE DATA; SATELLITE IMAGERY; TRAINING;

EID: 84888002808     PISSN: 0255660X     EISSN: 09743006     Source Type: Journal    
DOI: 10.1007/s12524-013-0286-z     Document Type: Article

References (27)

1. Benediktsson, J. A., & Kanellopoulos, I. (1999). Classification of multisource and hyperspectral data based on decision fusion. IEEE Transactions on Geoscience and Remote Sensing, 37(3), 1367-1377.
2. Camps-Valls, G., & Bruzzone, L. (2005). Kernel-based methods for hyperspectral image classification. IEEE Transaction on Geoscience and Remote Sensing, 43(6), 1351-1362.
3. Ceamanos, X., Waske, B., Benediktsson, J., Chanussot, J., Fauvel, M., & Sveinsson, J. (2010). A classifier ensemble based on fusion of support vector machines for classifying hyperspectral data. International Journal of Image and Data Fusion, 1(4), 293-307.
4. Del Frate, F., Pacifici, F., Schiavon, G., & Solimini, C. (2007). Use of neural networks for automatic classification from high-resolution images. IEEE Transactions on Geoscience and Remote Sensing, 45(4), 800-809.
5. Goebel, K., Yan, W., & Cheetham, W. (2002). A Method to Calculate Classifier Correlation for Decision Fusion, Proceedings of IDC (Information, Decision Control Conference) 2002, Adelaide, pp. 135-140.
6. Goel, P. K., Prasher, S. O., Patel, R. M., Landry, J. A., Bonnel, R. B., & Viau, A. A. (2003). Classification of hyperspectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn. Computers and Electronics in Agriculture, 39(2), 67-93.
7. Guo, B., Gunn, S. R., Damper, R. I., & Nelson, J. D. B. (2006). Band selection for hyperspectral image classification using mutual information. IEEE Geoscience and Remote Sensing Letters, 3(4), 522-526.
8. Hsu, C.-W., Chung, C.-C., & Lin, C.-J. (2010). A Practical Guide to Support Vector Classification. National Taiwan University, March 13, 2010 [Online]. Available: www. csie. ntu. edu. tw/_cjlin.
9. Imbault, F., & Lebart, K. (2004). A stochastic optimization approach for parameter tuning of support vector machines, Proceedings of the 17th International Conference on Pattern Recognition (ICPR 2004), 4, 597-600.
10. Jia, X. (2002). Simplified maximum likelihood classification for hyperspectral data in cluster space. IEEE International Geoscience and Remote Sensing Symposium, 2002 (IGARSS '02), 5, 2578-2580.
11. Kuncheva, L. (2004). Combining Pattern Classifiers methods and algorithms. Hoboken: John Wiley & Sons, INC. Publication.
12. Kuncheva, L., & Whitaker, C. (2003). Measures of diversity in classifier ensemble and their relationship with the ensemble accuracy. Machine Learning, 51(2), 181-207.
13. Li, S., Wu, H., Wan, D., & Zhu, J. (2011). An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine. Knowledge-Based Systems, 24(1), 40-48.
14. Martinez-Uso, A., Pla, F., Sotoca, J. M., & Garcia-Sevilla, P. (2006). Clustering based multispectral band selection using mutual information. In Proceedings of the 18th International Conference on Pattern Recognition (ICPR). 2, 760-763.
15. Melgani, F., & Bruzzone, L. (2004). Classification of hyperspectral remote sensing images with support vector machines. IEEE Transaction on Geosciences and Remote Sensing, 42(8), 1778-1790.
16. Müller, K. L., Mika, S., Rätsch, G., Tsuda, K., & Schölkopf, B. (2001). An introduction to kernel-based learning algorithms. IEEE Transaction on Neural Network, 12(2), 181-202.
17. Prasad, S., & Bruce, L. M. (2008). Decision fusion with confidence based weight assignment for hyperspectral target recognition. IEEE Transaction on Geosciences and Remote Sensing, 46(5), 1448-1456.
18. Pudil, P., Novovicova, P., & Kittler, J. (1994). Floating search methods in feature selection. Pattern Recognition Letter, 15(11), 1119-1125.
19. Ruta, D., & Gabrys, B. (2000). An overview of classifier fusion methods. Computing and Information Systems, 7(1), 1-10.
20. Scholkopf, B., & Smola, A. J. (2002). Learning with kernels, support vector machines, regularization, optimization and beyond. Cambridge: MIT Press.
21. Tarabalka, Y., Fauvel, M., Chanussot, J., & Benediktsson, J. (2010). SVM- and MRF-based method for accurate classification of hyperspectral images. IEEE Geosciences and Remote Sensing letters, 7(4), 736-740.
22. Vaiphasa, C. (2003). Innovative genetic algorithm for hyperspectral image classification, In Proceeding of International Conference of Map Asia, pp. 20.
23. Vapnik, V. N. (1998). Statistical learning theory. New York: Wiley.
24. Waske, B., & van der Linden, S. (2008). Classifying multilevel imagery from SAR and optical sensors by decision fusion. IEEE Transactions on Geosciences and Remote Sensing, 46(5), 1457-1466.
25. Watanachaturaporn, P., & Arora, M. K. (2004). Support vector machines for classification of multi- and hyperspectral data. In P. K. Varshney & M. K. Arora (Eds.), Advanced image processing techniques for remotely sensed hyperspectral data (pp. 237-255): Springer-Verlag.
26. Weston, J., & Watkins, C. (1999). Support vector machines for multiclass pattern recognition. In The Seventh European Symposium on Articial Neural Networks, pp. 219-224.
27. Xu, L., Krzyzak, A., & Suen, C. Y. (1992). Methods of combining multiple classifiers and their applications to handwriting recognition. IEEE Transactions on Systems, Man, and Cybernetics, 22(3), 418-435.