Remote sensing feature selection by kernel dependence measures

IEEE Geoscience and Remote Sensing Letters

Volumn 7, Issue 3, 2010, Pages 587-591

  Camps Valls, Gustavo ^a   Mooij, Joris ^b   Schölkopf, Bernhard ^b  

^a UNIVERSITY OF VALENCIA   (Spain)

^b MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

Dependence estimation; feature selection; image classification; kernel methods; support vector machine (SVM)

Indexed keywords

CLASS LABELS; CROSS-COVARIANCE; DEPENDENCE MEASURES; FEATURE SELECTION; HILBERT-SCHMIDT NORM; HYPERSPECTRAL; KERNEL METHODS; MULTI-SPECTRAL; NONLINEAR MEASURE; P-VALUES; SAR DATA; STATISTICAL DEPENDENCE; SUPPORT VECTOR MACHINE (SVM);

ESTIMATION; HILBERT SPACES; IMAGE ANALYSIS; IMAGE CLASSIFICATION; OPTIMIZATION; RANDOM VARIABLES; REMOTE SENSING; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

EID: 77954623833     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2010.2041896     Document Type: Article

References (22)

1. A. Blum and P. Langley, "Selection of relevant features and examples in machine learning," Artif. Intell., vol. 97, no. 1/2, pp. 245-271, Dec. 1997.
2. R. Kohavi and G. H. John, "Wrappers for features subset selection," Artif. Intell., vol. 97, no. 1/2, pp. 273-324, Dec. 1997.
3. S. B. Serpico and G. Moser, "Extraction of spectral channels from hyperspectral images for classification purposes," IEEE Trans. Geosci. Remote Sens., vol.45, no.2, pp. 484-495, Feb. 2007.
4. B. Paskaleva,M.M. Hayat, Z.Wang, J. S. Tyo, and S. Krishna, "Canonical correlation feature selection for sensors with overlapping bands: Theory and application," IEEE Trans. Geosci. Remote Sens., vol.46, no.10, pp. 3346-3358, Oct. 2008.
5. S. B. Serpico and L. Bruzzone, "A new search algorithm for feature selection in hyperspectral remote sensing images," IEEE Trans. Geosci. Remote Sens., vol.39, no.7, pp. 1360-1367, Jul. 2001.
6. R. Archibald and G. Fann, "Feature selection and classification of hyperspectral images with support vector machines," IEEE Geosci. Remote Sens. Lett., vol.4, no.4, pp. 674-679, Oct. 2007.
7. Y. Bazi and F. Melgani, "Toward an optimal SVM classification system for hyperspectral remote sensing images," IEEE Trans. Geosci. Remote Sens., vol.44, no.11, pp. 3374-3385, Nov. 2006.
8. A. Gretton, O. Bousquet, A. J. Smola, and B. Schölkopf, "Measuring statistical dependence with Hilbert-Schmidt norms," in Proc. Algorithmic Learn. Theory, S. Jain and W.-S. Lee, Eds., 2005, pp. 63-77.
9. L. Song, A. J. Smola, A. Gretton, K. M. Borgwardt, and J. Bedo, "Supervised feature selection via dependence estimation," in Proc. Int. Conf. Mach. Learn., C. Sammut and Z. Ghahramani, Eds., 2007, pp. 823-830.
10. L. Song, J. Bedo, K. M. Borgwardt, A. Gretton, and A. J. Smola, "Gene selection via the BAHSIC family of algorithms," Bioinformatics (ISMB), vol.23, no.13, pp. i490-i498, Jul. 2007.
11. G. Camps-Valls and L. Bruzzone, Eds., Kernel Methods for Remote Sensing Data Analysis. London, U.K.: Wiley, Nov. 2009.
12. L. Song, "Learning via Hilbert space embedding of distributions," Ph.D. dissertation, School Inf. Technol., Univ. Sydney, Sydney, Australia, 2008.
13. C. Baker, "Joint measures and cross-covariance operators," Trans. Amer. Math. Soc., vol.186, pp. 273-289, Dec. 1973.
14. K. Fukumizu, F. R. Bach, and M. I. Jordan, "Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces," J. Mach. Learn. Res., vol.5, pp. 73-99, 2004.
15. N. L. Johnson, S. Kotz, and N. Balakrishnan, Continuous Univariate Distributions Extraction: Foundations and Applications, 2nd ed. Hoboken, NJ: Wiley, 1994.
16. I. Steinwart, "On the influence of the kernel on the consistency of support vector machines," J. Mach. Learn. Res., vol.2, pp. 67-93, 2001.
17. L. Gómez-Chova, D. Fernández-Prieto, J. Calpe, E. Soria, J. Vila-Francés, and G. Camps-Valls, "Urban monitoring using multitemporal SAR and multispectral data," Pattern Recognit. Lett., vol.27, no.4, pp. 234-243, Mar. 2006.
18. Q. Jackson and D. A. Landgrebe, "An adaptive classifier design for high-dimensional data analysis with a limited training data set," IEEE Trans. Geosci. Remote Sens., vol.39, no.12, pp. 2664-2679, Dec. 2001. (Pubitemid 34091845)
19. I. Guyon, J. Weston, S. Barnhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Mach. Learn., vol.46, no.1-3, pp. 389-422, Jan. 2002.
20. J. Weston, A. Elisseeff, B. Scholkopf, M. Tipping, and P. Kaelbling, "Use of the zero-norm with linear models and kernel methods," J. Mach. Learn. Res., vol.3, pp. 1439-1461, 2003.
21. G. M. Foody, "Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy," Photogramm. Eng. Remote Sens., vol.70, no.5, pp. 627-663, May 2004.
22. A. Statnikov, D. Hardin, and C. Aliferis, "Using SVM weight-based methods to identify causally relevant and non-causally relevant variables," in Proc. NIPS. Workshop Causality Feature Selection, 2006, pp. 129-150.