Locality-preserving dimensionality reduction and classification for hyperspectral image analysis

IEEE Transactions on Geoscience and Remote Sensing

Volumn 50, Issue 4, 2012, Pages 1185-1198

  Li, Wei ^a   Prasad, Saurabh ^a   Fowler, James E ^a   Bruce, Lori Mann ^a  

^a MISSISSIPPI STATE UNIVERSITY   (United States)

Author keywords

Dimensionality reduction; Gaussian mixture model (GMM); hyperspectral data; local discriminant analysis; support vector machine

Indexed keywords

DIMENSIONALITY REDUCTION; ELECTROMAGNETIC SPECTRA; FISHER'S DISCRIMINANT; GAUSSIAN MIXTURE MODEL; GAUSSIAN-MIXTURE-MODEL (GMM); HIGH-DIMENSIONAL FEATURE SPACE; HYPERSPECTRAL; HYPERSPECTRAL DATA; HYPERSPECTRAL IMAGE ANALYSIS; HYPERSPECTRAL IMAGERY; ILL-CONDITIONED; LINEAR DISCRIMINANT ANALYSIS; MAXIMUM LIKELIHOOD CLASSIFIERS; MULTI-MODAL DATA; MULTIMODAL STRUCTURE; STATISTICAL STRUCTURES; WEALTH OF INFORMATION;

DISCRIMINANT ANALYSIS; GAUSSIAN DISTRIBUTION; IMAGE SEGMENTATION; MAXIMUM LIKELIHOOD ESTIMATION; PRINCIPAL COMPONENT ANALYSIS; REMOTE SENSING; SUPPORT VECTOR MACHINES;

IMAGE RETRIEVAL;

DATA SET; DISCRIMINANT ANALYSIS; ELECTROMAGNETIC FIELD; ELECTROMAGNETIC METHOD; GAUSSIAN METHOD; IMAGE ANALYSIS; IMAGE CLASSIFICATION; LINEARITY; PARADIGM SHIFT; PIXEL; PRINCIPAL COMPONENT ANALYSIS; SPECTRAL ANALYSIS;

EID: 84859784358     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2011.2165957     Document Type: Article

References (34)

1. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern Classification, 2nd ed. New York: Wiley, 2001.
2. C. Lee and D. A. Landgrebe, "Analyzing high-dimensional multispectral data," IEEE Trans. Geosci. Remote Sens., vol. 31, no. 4, pp. 792-800, Jul. 1993.
3. S. Prasad and L. M. Bruce, "Decision fusion with confidence-based weight assignment for hyperspectral target recognition," IEEE Trans. Geosci. Remote Sens., vol. 46, no. 5, pp. 1448-1456, May 2008.
4. L. Zhang, Y. Zhong, B. Huang, J. Gong, and P. Li, "Dimensionality reduction based on clonal selection for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 45, no. 12, pp. 4172-4186, Dec. 2007.
5. M. D. Farrell and R. M. Mersereau, "On the impact of PCA dimension reduction for hyperspectral detection of difficult targets," IEEE Geosci. Remote Sens. Lett., vol. 2, no. 2, pp. 192-195, Apr. 2005.
6. X. Jia and J. A. Richards, "Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification," IEEE Trans. Geosci. Remote Sens., vol. 37, no. 1, pp. 538-542, Jan. 1999.
7. S. Prasad and L. M. Bruce, "Limitations of principal component analysis for hyperspectral target recognition," IEEE Geosci. Remote Sens. Lett., vol. 5, no. 4, pp. 625-629, Oct. 2008.
8. S. Di Zenzo, R. Bernstein, S. D. Degloria, and H. C. Kolsky, "Gaussian maximum likelihood and contextual classification algorithms for multicrop classification," IEEE Trans. Geosci. Remote Sens., vol. GRS-25, no. 6, pp. 805-814, Nov. 1987.
9. A. M. Martinez and A. C. Kak, "PCA versus LDA," IEEE Trans. Pattern Anal. Mach. Intell., vol. 23, no. 2, pp. 228-233, Feb. 2001.
10. M. Sugiyama, "Dimensionality reduction of multimodal labeled data by local Fisher discriminant analysis," J. Mach. Learn. Res., vol. 8, no. 5, pp. 1027-1061, May 2007.
11. S. Tadjudin and D. A. Landgrebe, "Robust parameter estimation for mixture model," IEEE Trans. Geosci. Remote Sens., vol. 38, no. 1, pp. 439-445, Jan. 2000.
12. M. M. Dundar and D. A. Landgrebe, "A model-based mixture-supervised classification approach in hyperspectral data analysis," IEEE Trans. Geosci. Remote Sens., vol. 40, no. 12, pp. 2692-2699, Dec. 2002.
13. A. Berge and A. H. S. Solberg, "Structured Gaussian components for hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3386-3396, Nov. 2006.
14. C. Cortes and V. N. Vapnik, "Support vector networks," Mach. Learn., vol. 20, no. 3, pp. 273-297, Sep. 1995.
15. 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.
16. 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.
17. D. Tuia, G. Camps-Valls, G. Matasci, and M. Kanevski, "Learning relevant image features with multiple-kernel classifications," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 10, pp. 3780-3791, Oct. 2010.
18. X. He and P. Niyogo, "Locality preserving projections," in Advances in Neural Information Processing System, S. Thrun, L. Saul, and B. Schölkopf, Eds. Cambridge, MA: MIT Press, 2004.
19. M. N. Do, "Fast approximation of Kullback-Leibler distance for dependence trees and hidden Markov models," IEEE Signal Process. Lett., vol. 10, no. 4, pp. 115-118, Apr. 2003.
20. G. Shaw and D. Manolakis, "Signal processing for hyperspectral image exploitation," IEEE Signal Process. Mag., vol. 19, no. 1, pp. 12-16, Jan. 2002.
21. N. Vlassis and A. Likas, "A greedy EM algorithm for Gaussian mixture learning," Neural Process. Lett., vol. 15, no. 1, pp. 77-87, Feb. 2002.
22. A. P. Benavent, F. E. Ruiz, and J. M. S. Martínez, "EBEM: An entropybased EM algorithm for Gaussian mixture models," in Proc. IEEE Int. Conf. Pattern Recognit., New York, Jun. 2006, vol. 2, pp. 451-455.
23. H. Akaike, "A new look at the statistical model identification, " IEEE Trans. Autom. Control, vol. AC-19, no. 6, pp. 716-723, Dec. 1974.
24. G. Schwarz, "Estimating the dimension of a model," Ann. Stat., vol. 6, no. 2, pp. 461-464, Mar. 1978.
25. R. J. Steele and A. E. Raftery, "Performance of Bayesian Model Selection Criteria for Gaussian Mixture Models," Dept. Stat., Univ. Washington, Washington, DC, Tech. Rep. 559, Sep. 2009.
26. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
27. I. Guyon, J. Weston, S. Barhill, and V. Vapnik, "Gene selection for cancer classification using support vector machines," Mach. Learn., vol. 46, no. 1-3, pp. 389-422, Jan. 2002.
28. 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.
29. P. Gamba, "A collection of data for urban area characterization, " in Proc. IEEE Int. Geosci. Remote Sens. Symp., Anchorage, AK, Sep. 2004, vol. 1, pp. 69-72.
30. T. Su and J. G. Dy, "In search of deterministic methods for initializing kmeans and Gaussian mixture clustering," Intell. Data Anal., vol. 11, no. 4, pp. 319-338, Sep. 2007.
31. T. V. Bandos, L. Bruzzone, and G. Camps-Valls, "Classification of hyperspectral images with regularized linear discriminant analysis," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 3, pp. 862-873, Mar. 2009.
32. W. Li, S. Prasad, J. E. Fowler, and L. M. Bruce, "Locality- preserving discriminant analysis in kernel-induced spaces for hyperspectral image classification," IEEE Geosci. Remote Sens. Lett., vol. 8, no. 5, pp. 894-898, Sep. 2011.
33. N. Dobigeon, S. Moussaoui, M. Coulon, J. Y. Tourneret, and A. O. Hero, "Joint Bayesian endmember extraction and linear unmixing for hyperspectral imagery," IEEE Trans. Signal Process., vol. 57, no. 11, pp. 4355-4368, Nov. 2009.
34. F. A. Mianji and Y. Zhang, "Robust hyperspectral classification using relevance vector machine," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 6, pp. 2100-2112, Jun. 2011.