Multiple Classifier Systems for Hyperspectral Remote Sensing Data Classification

Journal of the Indian Society of Remote Sensing

Volumn 42, Issue 2, 2014, Pages 423-428

  Khosravi, Iman ^a   Mohammad Beigi, Majid ^a  

^a UNIVERSITY OF ISFAHAN   (Iran)

Author keywords

Correlation based feature selection; Hyperspectral data classification; Multiple classifier system; Support vector machine

Indexed keywords

ACCURACY ASSESSMENT; AVIRIS; REMOTE SENSING; SATELLITE DATA; SPECTRAL ANALYSIS;

EID: 84901603260     PISSN: 0255660X     EISSN: 09743006     Source Type: Journal    
DOI: 10.1007/s12524-013-0327-7     Document Type: Article

References (18)

1. Available online at: http://en. wikipedia. org/wiki/Support_vector_machine/. Accessed 30 December 2012.
2. 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.
3. Benediktsson, J. A., Chanussot, J., & Fauvel, M. (2007). Multiple classifier systems in remote sensing: from basics to recent developments. MCS'07 Proceedings of the 7th international conference on multiple classifier systems. Heidelberg, Germany: Springer.
4. Bigdeli, B., Samadzadegan, F., & Reinartz, P. (2013). A multiple SVM system for classification of Hyperspectral remote sensing data. Journal of the Indian Society of Remote Sensing, May 2013. doi: 10. 1007/s12524-013-0286-z.
5. Camps-Valls, G., & Bruzzone, L. (2005). Kernel-based methods for hyperspectral image classification. IEEE Transactions on Geoscience and Remote Sensing, 43(6), 1351-1362.
6. Ceamanos, X., Waske, B., Benediktsson, J. A., Chanussot, J., Fauvel, M., & Sveinsson, J. R. (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.
7. Fauvel, M., Chanussot, J., & Benediktsson, J. A. (2006). A combined support vector machines classification based on decision fusion. IEEE International on Geoscience and Remote Sensing Symposium, 2494-2497.
8. Fauvel, M., Benediktsson, J. A., Chanussot, J., & Sveinsson, J. R. (2008). Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles. IEEE Transactions on Geoscience and Remote Sensing, 46(11), 4834-4837.
9. Gualtieri, J. A., & Chettri, S. (2000). Support vector machines for classification of hyperspectral data. in Proc. IGARSS, Honolulu, HI, 2, 813-815.
10. Hall, M. A. (1998). Correlation-based feature selection for machine learning, PhD thesis, Department of Computer Science, University of Waikato, Hamilton, New Zealand.
11. Ham, J., Chen, Y., Crawford, M. M., & Gosh, J. (2005). Investigation of the random forest framework for classification of hyperspectral data. IEEE Transactions on Geoscience and Remote Sensing, 43(3), 492-501.
12. Haykin, S. (2005). Neural networks: A comprehensive foundation. Prentice-Hall, 340-360.
13. Hsu, C.-W., & Lin, C.-J. (2002). A comparison of methods for multiclass support vector machines. IEEE Transactions on Neural Networks, 13(2), 415-425.
14. Lopez, F. G., Torres, M. G., Batista, B. M., Perez, J. A. M., & Moreno-Vega, J. M. (2006). Solving feature subset selection problem by a parallel scatter search. European Journal of Operational Research, 169, 477-489.
15. Malleswara Rao, T. C., Jai Sankar, G., & Roopesh Kumar, T. (2012). A hierarchical hybrid SVM method for classification of remotely sensed data. Journal of the Indian Society of Remote Sensing, 40(2), 191-200.
16. Pal, M., & Mather, P. M. (2004). Assessment of the effectiveness of support vector machines for hyperspectral data. Future Generation Computer Systems, 20(7), 1215-1225.
17. Waske, B., & Benediktsson, J. A. (2007). Fusion of support vector machines for classification of multisensor data. IEEE Transaction on Geoscience and Remote Sensing, 45(12), 3858-3866.
18. Waske, B., & Vander 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.