Learning relevant image features with multiple-kernel classification

IEEE Transactions on Geoscience and Remote Sensing

Volumn 48, Issue 10, 2010, Pages 3780-3791

  Tuia, Devis ^a   Camps Valls, Gustavo ^b   Matasci, Giona ^a   Kanevski, Mikhail ^a  

^a UNIVERSITY OF LAUSANNE   (Switzerland)

^b UNIVERSITY OF VALENCIA   (Spain)

Author keywords

Feature selection; image classification; kernel alignment; multiple kernel learning (MKL); ranking; recursive feature elimination (RFE); support vector machine (SVM)

Indexed keywords

FEATURE SELECTION; KERNEL ALIGNMENT; MULTIPLE-KERNEL LEARNING (MKL); RANKING; RECURSIVE FEATURE ELIMINATION; SUPPORT VECTOR MACHINE (SVM);

ALIGNMENT; IMAGE ANALYSIS; IMAGE CLASSIFICATION; IMAGE RECONSTRUCTION; INFORMATION USE; OPTIMIZATION; REMOTE SENSING; SPACE OPTICS; SUPPORT VECTOR MACHINES;

FEATURE EXTRACTION;

AUTOMATION; DATA QUALITY; IMAGE ANALYSIS; IMAGE CLASSIFICATION; OPTIMIZATION; RANKING; REMOTE SENSING; SATELLITE SENSOR; SPECTRAL RESOLUTION;

EID: 77957001686     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2010.2049496     Document Type: Article

References (55)

1. T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning, 2nd ed. New York: Springer-Verlag, 2001, ser. Series in Statistics.
2. F. Melgani and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Trans. Geosci. Remote Sens., vol.42, no.8, pp. 1778-1790, Aug. 2004.
3. 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.
4. M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. R. Sveinsson, "Spectral and spatial classification of hyperspectral data using SVMs and morphological profiles," IEEE Trans. Geosci. Remote Sens., vol.46, no.11, pp. 3804-3814, Nov. 2008.
5. G. Camps-Valls and L. Bruzzone, Eds., Kernel Methods in Remote Sensing Image Processing. Hoboken, NJ: Wiley, 2009.
6. G. F. Hughes, "On the mean accuracy of statistical pattern recognition," IEEE Trans. Inf. Theory, vol.IT-14, no.1, pp. 55-63, Jan. 1968.
7. L. Bruzzone, R. Cossu, and G. Vernazza, "Detection of land-cover transitions by combining multidate classifiers," Pattern Recognit. Lett., vol.25, no.13, pp. 1491-1500, Oct. 2004.
8. B. Waske and J. A. Benediktsson, "Fusion of support vector machines for classification of multisensor data," IEEE Trans. Geosci. Remote Sens., vol.45, no.12, pp. 3858-3866, Dec. 2007.
9. G. Camps-Valls, L. Gómez-Chova, J. Muñoz-Marí, J. L. Rojo-Álvarez, and M. Martínez-Ramón, "Kernel-based framework for multi-temporal andmulti-source remote sensing data classification and change detection," IEEE Trans. Geosci. Remote Sens., vol.46, no.6, pp. 1822-1835, Jun. 2008.
10. L. Bruzzone and S. B. Serpico, "An iterative technique for the detection of land-cover transitions in multitemporal remote-sensing images," IEEE Trans. Geosci. Remote Sens., vol.35, no.4, pp. 858-867, Jul. 1997.
11. L. Bruzzone and D. Fernández Prieto, "A partially unsupervised cascade classifier for the analysis of multitemporal remote-sensing images," Pattern Recognit. Lett., vol.23, no.9, pp. 1063-1071, Jul. 2002.
12. D. Clausi, "Comparison and fusion of co-occurrence, Gabor, and MRF texture features for classification of SAR sea ice imagery," Atmos. Oceans, vol.39, no.4, pp. 183-194, 2001.
13. J. A. Benediktsson, M. Pesaresi, and K. Arnason, "Classification and feature extraction for remote sensing images from urban areas based on morphological transformations," IEEE Trans. Geosci. Remote Sens., vol.41, no.9, pp. 1940-1949, Sep. 2003.
14. A. Plaza, P. Martínez, J. Plaza, and R. Pérez, "Dimensionality reduction and classification of hyperspectral image data using sequences of extended morphological transformations," IEEE Trans. Geosci. Remote Sens., vol.43, no.3, pp. 466-479, Mar. 2005.
15. F. Pacifici, M. Chini, and W. Emery, "A neural network approach using multi-scale textural metrics from very high resolution panchromatic imagery for urban land-use classification," Remote Sens. Environ., vol.113, no.6, pp. 1276-1292, Jun. 2009.
16. I. Guyon, S. Gunn, M. Nikravesh, and L. A. Zadeh, Feature Extraction: Foundations and Applications. New York: Springer-Verlag, 2006.
17. I. Guyon and A. Elisseeff, "An introduction to variable and feature selection," J. Mach. Learn. Res., vol. 3, no. 7/8, pp. 1157-1182, Mar. 2003.
18. T. A. Warner, K. Steinmaus, and H. Foote, "An evaluation of spatial autocorrelation feature selection," Int. J. Remote Sens., vol.20, no.8, pp. 1601-1616, May 1999.
19. J. S. Borak, "Feature selection and land cover classification of a MODISlike data set for a semiarid environments," Int. J. Remote Sens., vol.20, no.5, pp. 919-938, Mar. 1999.
20. L. Bruzzone and S. Serpico, "A technique for features selection in multiclass problems," Int. J. Remote Sens., vol.21, no.3, pp. 549-563, Feb. 2000.
21. T. Kavzoglu and P. M. Mather, "The role of feature selection in artificial neural network applications," Int. J. Remote Sens., vol.23, no.15, pp. 2919-2937, Aug. 2002.
22. B. Demir and S. Ertürk, "Phase correlation based redundancy removal in feature weighting band selection for hyperspectral images," Int. J. Remote Sens., vol.29, no.6, pp. 1801-1807, Mar. 2008.
23. B.-C. Kuo, C.-H. Li, and J.-M. Yang, "Kernel nonparametric weighted feature extraction for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol.47, no.4, pp. 1139-1155, Apr. 2009.
24. 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.
25. J. Weston, A. Elisseeff, B. Schölkopf, and M. Tipping, "Use of the zeronorm with linear models and kernel methods," J. Mach. Learn. Res., vol.3, pp. 1439-1461, Mar. 2003.
26. D. Tuia, F. Pacifici, M. Kanevski, and W. Emery, "Classification of very high spatial resolution imagery using mathematical morphology and support vector machines," IEEE Trans. Geosci. Remote Sens., vol.47, no.11, pp. 3866-3879, Nov. 2009.
27. 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. 674-677, Oct. 2007.
28. 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.
29. S. S. Durbha, R. L. King, and N. H. Younan, "Wrapper-based feature subset selection for rapid image information mining," IEEE Geosci. Remote Sens. Lett., vol.7, no.1, pp. 43-47, Jan. 2010.
30. L. Zhang, Y. Zhong, B. Huang, and P. Li, "Dimensionality reduction based on clonal selection for hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol.45, no.12, pp. 4172-4186, Dec. 2007.
31. P. Zhong, P. Zhang, and R.Wang, "Dynamic learning of SMLR for feature selection and classification of hyperspectral data," IEEE Geosci. Remote Sens. Lett., vol.5, no.2, pp. 280-284, Apr. 2008.
32. G. Lanckriet, T. De Bie, N. Cristianini, M. Jordan, and W. Noble, "A statistical framework for genomic data fusion," Bioinformatics, vol.20, no.16, pp. 2626-2635, Nov. 2004.
33. S. Sonnenburg, C. S. G. Rätsch, and B. Schölkopf, "Large scale multiple kernel learning," J. Mach. Learn. Res., vol.7, pp. 1531-1565, 2006.
34. G. Camps-Valls, L. Gómez-Chova, J. Muñoz-Marí, J. Vila-Francés, and J. Calpe-Maravilla, "Composite kernels for hyperspectral image classification," IEEE Geosci. Remote Sens. Lett., vol.3, no.1, pp. 93-97, Jan. 2006. (Pubitemid 43131904)
35. M. Marconcini, G. Camps-Valls, and L. Bruzzone, "A composite semisupervised SVM for classification of hyperspectral images," IEEE Geosci. Remote Sens. Lett., vol.6, no.2, pp. 234-238, Apr. 2009.
36. M. Fauvel, J. Chanussot, and J. A. Benediktsson, "A joint spatial and spectral SVM's classification of panchromatic images," in Proc. IEEE IGARSS, Barcelona, Spain, Jul. 2007, pp. 1497-1500.
37. D. Tuia, F. Ratle, A. Pozdnoukhov, and G. Camps-Valls, "Multi-source composite kernels for urban image classification," IEEE Geosci. Remote Sens. Lett., vol.7, no.1, pp. 88-92, Jan. 2010.
38. A. Villa, M. Fauvel, J. Chanussot, P. Gamba, and J. A. Benediktsson, "Gradient optimization for multiple kernel parameters in support vector machines classification," in Proc. IEEE IGARSS, 2008, pp. IV-224-IV-227.
39. B. Guo, S. Gunn, R. I. Damper, and J. D. B. Nelson, "Customizing kernel functions for SVM-based hyperspectral image classification," IEEE Trans. Image Process., vol.17, no.4, pp. 622-629, Apr. 2008.
40. F. Bach, G. Lanckriet, and M. Jordan, "Multiple kernel learning, conic duality, and the SMO algorithm," in Proc. Int. Conf. Mach. Learn., 2004, pp. 41-48.
41. S. V. N. Vishwanathan, A. J. Smola, and M. Murty, "Simple SVM," in Proc. Int. Conf. Mach. Learn., 2003, vol.13, pp. 682-688.
42. A. Zien and C. S. Ong, "Multiclass multiple kernel learning," in Proc. ICML, Corvallis, OR, 2007, vol.227, pp. 1191-1198.
43. A. Rakotomamonjy, F. R. Bach, S. Canu, and Y. Grandvalet, "Simple MKL," J. Mach. Learn. Res., vol.9, pp. 2491-2521, 2008.
44. N. Cristianini, J. Kandola, A. Elisseeff, and J. Shawe-Taylor, "On kernel target alignment," Roy. Holloway College, Univ. London, London, U.K., NeuroCOLT, Tech. Rep. 2001-087, 2001.
45. C. Igel, T. Glasmachers, B. Mersch, N. Pfeifer, and P. Meinicke, "Gradient-based optimization of kernel-target alignment for sequence kernels applied to bacterial gene start detection," IEEE Trans. Comput. Biol. Bioinformatics, vol.4, no.2, pp. 216-226, Apr.-Jun. 2007.
46. A. Kumar and C. Sminchisescu, "Support kernel machines for object recognition," in Proc. IEEE ICCV, 2007, pp. 1-8.
47. B. Schölkopf and A. J. Smola, Learning With Kernels: Support Vector Machines, Regularization, Optimization and Beyond. Cambridge, MA: MIT Press, 2002.
48. J. Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis. Cambridge, U.K.: Cambridge Univ. Press, 2004.
49. B. Boser, I. Guyon, and V. Vapnik, "A training algorithm for optimal margin classifiers," in Proc. 5th ACM Workshop Comput. Learn. Theory, Pittsburgh, PA, 1992, pp. 144-152.
50. T. M. Cover, "Geometrical and statistical properties of systems of linear inequalities with application in pattern recognition," IEEE Trans. Electron. Comput., vol.EC-14, no.3, pp. 326-334, Jun. 1965, reprinted in Artificial Neural Networks: Concepts and Theory, IEEE Computer Society Press, Los Alamitos, California, 1992, Eds. P. Mehra and B. Wah.
51. J. Mercer, "Functions of positive and negative type and their connection with the theory of integral equations," Philos. Trans. R. Soc. Lond. Ser. A, vol. CCIX, no. A456, pp. 215-228, May 1905.
52. G. Camps-Valls, L. Gómez-Chova, J. Calpe, and E. Soria, "Robust support vector method for hyperspectral data classification and knowledge discovery," IEEE Trans. Geosci. Remote Sens., vol.42, no.7, pp. 1530- 1542, Jul. 2004.
53. A. Fanelli, M. Santoro, A. Vitale, P. Murino, and J. Askne, "Understanding ERS coherence over urban areas," in Proc. ERS-Envisat Symp.- Looking Down to Earth in the New Millennium, Gothenburg, Sweden, Oct. 16-20, 2000. [CD-ROM].
54. L. Gómez-Chova, D. Fernández-Prieto, J. Calpe, E. Soria, J. Vila, and G. Camps-Valls, "Urban monitoring using multitemporal SAR and multispectral data," Pattern Recognit. Lett., vol.27, no.4, pp. 234-243, Mar. 2006.
55. G. M. Foody, "Thematic map comparison: Evaluating the statistical significance of differences in classification accuracy," Photogramm. Eng. Remote Sens., vol.70, no.5, pp. 627-633, May 2004.