Feature selection and classification of hyperspectral images with support vector machines

IEEE Geoscience and Remote Sensing Letters

Volumn 4, Issue 4, 2007, Pages 674-677

  Archibald, Rick ^a   Fann, George ^a  

^a OAK RIDGE NATIONAL LABORATORY   (United States)

Author keywords

Feature selection; Hyperspectral images; Support vector machines (SVMs)

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; COST EFFECTIVENESS; FEATURE EXTRACTION; KNOWLEDGE ACQUISITION; PROBLEM SOLVING;

CLASSIFICATION METHOD; HYPERSPECTRAL IMAGES; INFORMATION REPRESENTATION;

SUPPORT VECTOR MACHINES;

EID: 35348920168     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2007.905116     Document Type: Article

References (12)

1. M. Boardman and T. Trappenberg, "International Joint Conference on Neural Networks," Vancouver, BC, Canada, Jul. 2006.
2. G. Camps-Valls and L. Bruzzone, "Kernel-based methods for hyperspectral image classification," IEEE Trans. Geosci., Remote Sens., vol. 43, no. 6, pp. 1351-1362, Jun. 2005.
3. O. Chapelle, "Training a support vector machine in the primal," Neural Comput., vol. 19, no. 5, pp. 1155-1178, 2007.
4. G. M. Foody and A. Mathur, "The use of small training sets containing mixed pixels for accurate hard image classification: Training on mixed spectral responses for classification by a SVM," Remote Sens. Environ., vol. 103, no. 2, pp. 179-189, Jul. 2006.
5. Y. Freund and R. E. Schapire, "A decision-theoretic generalization of online learning and an application to boosting," J. Comput. Syst. Sci., vol. 55, no. 1, pp. 119-139, Aug. 1997.
6. I. M. Guyon, J. Weston, S. Barnhill, and V. N. Vapnik, "Gene selection for cancer classification using support vector machines," Mach. Learn., vol. 46, no. 1-3, pp. 389-422, Jan. 2002.
7. G. F. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE Trans. Inf. Theory, vol. IT-14, no. 1, pp. 55-63, Jan. 1968.
8. T. Joachims, "Making large-scale SVM learning practical," in Advances in Kernel Methods: Support Vector Learning, B. Schölkopf, C. Burges, and A. Smola, Eds. Cambridge, MA: MIT Press, 1999, ch. 11.
9. D. Landgrebe, AVIRIS NW Indianas Indian Pines 1992 data set, 1992. [Online]. Available: ftp://ftp.ecn.purdue.edu/biehl/MultiSpec/92AV3C.lan (original files) and ftp://ftp.ecn.purdue.edu/biehl/PC_MultiSpec/ThyFiles.zip (ground truth)
10. R. Rifkin and A. Klautau, "In defense of one-vs-all classification," J. Mach. Learn. Res., vol. 5, pp. 101-141, 2004.
11. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
12. J. Weston, A. Elisseeff, and B. Schölkopf, "Use of the zero norm with linear models and kernel methods," J. Mach. Learn. Res., vol. 3, pp. 1439-1461, 2003.