Some recent results on hyperspectral image classification

2003 IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data

Volumn , Issue , 2004, Pages 346-353

  Shah, C A ^a   Watanachaturaporn, P ^a   Varshney, P K ^a   Arora, M K ^b  

^a Syracuse University   (United States)

^b INDIAN INSTITUTE OF TECHNOLOGY ROORKEE   (India)

Author keywords

classification; hyperspectral images; ICA mixture model; independent componenet analysis; support vector machines

Indexed keywords

ALGORITHMS; CLASSIFICATION (OF INFORMATION); INDEPENDENT COMPONENT ANALYSIS; LAGRANGE MULTIPLIERS; MIXTURES; REMOTE SENSING; SPECTROSCOPY; SUPPORT VECTOR MACHINES;

HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGE CLASSIFICATION; ICA MIXTURE MODEL; INDEPENDENT COMPONENET ANALYSIS; INDEPENDENT COMPONENT ANALYSIS(ICA); LAGRANGIAN OPTIMIZATION METHODS; SUPPORT VECTOR MACHINE (SVMS); UNSUPERVISED CLASSIFICATION;

IMAGE CLASSIFICATION;

EID: 84871449098     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WARSD.2003.1295214     Document Type: Conference Paper

References (31)

1. D. Landgrebe, "Hyperspectral image data analysis," IEEE Signal Process. Mag., vol. 19, pp. 17-28, Jan. 2002.
2. X. Jia and J. A. Richards, Remote sensing digital image analysis: An introduction. 3rd ed., Berlin, NY: Springer Verlag, 1999.
3. J. R. Jensen, Introductory digital image processing: A remote sensing perspective. 2nd ed., Upper Saddle River, NJ: Prentice Hall, 1995.
4. G. Shaw and D. Manolakis, "Signal processing for hyperspectral image exploitation," IEEE Signal Process. Mag., vol. 19, pp. 12-16, Jan. 2002.
5. T.-W. Lee, M. S. Lewicki, and T. J. Sejnowski, "ICA mixture models for unsupervised classification of non-Gaussian classes and automatic context switching in blind signal separation," IEEE Trans. Pattern Anal. Machine Intell., vol. 22, pp. 1078-1089, Oct. 2000.
6. C. A. Shah, M. K. Arora, S. A. Robila, and P. K. Varshney, "ICA mixture model based unsupervised classification of hyperspectral imagery", IEEE Proceedings of 31st International Workshop on Applied Imagery and Pattern Recognition, 29-35, 2002.
7. T-W. Lee, M. Girolami, and T. J. Sejnowski, "Independent component analysis using an extended infomax algorithm for mixed sub-Gaussian and super-Gaussian sources," Neural Computation, vol. 11, pp. 417-441, 1999.
8. B. E. Boser, I. M. Guyon, and V. N. Vapnik, "A training algorithm for optimal margin classifiers," in Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, D. Haussler, Ed. Pittsburgh, PA: ACM Press, 1992, pp. 144-152.
9. C. Cortes, and V. N. Vapnik, "Support vector network," Machine Learning, vol. 200, 1995, pp. 273-297.
10. V. N. Vapnik, The Nature of Statistical Learning Theory. New York: Springer, 1995.
11. V. N. Vapnik, Statistical Learning Theory. New York: John Wiley and Sons, 1998.
12. V. David Sánchez A., "Advanced support vector machines and kernel methods," Neurocomputing, vol. 55, 2003, pp. 5-20.
13. R. O. Duda, P. E. Hart, and D. G. Stork, Pattern classification, 2nd ed., NY: John Wiley & Sons, 2000.
14. C. A. Shah, "ICA mixture model algorithm for unsupervised classification of multi/hyperspectral imagery," M.S. Thesis, EECS Dept., Syracuse University, Syracuse, NY, 2003.
15. S. A. Robila and P. K. Varshney, "A fast source separation algorithm for hyperspectral image processing", in proc. IEEE International Geoscience and Remote Sensing Symposium, 24th Canadian Symposium on Remote Sensing, 2002, pp. 3516-3518.
16. C.-I. Chang, S.-S. Chiang, J. A. Smith, and I. W. Ginsberg, "Linear spectral random mixture analysis for hyperspectral imagery," IEEE Trans. Geosci. Remote Sensing, vol. 40, pp. 375-392, Feb. 2002.
17. J. A. Richards and X. Jia, "Segmented principal components transformation for efficient hyperspectral remote sensing image display and classification," IEEE Trans. Geosci. Remote Sens., vol. 37, pp. 538-542, Jan. 1999.
18. J. C. Harsanyi and C. I. Chang, "Hyperspectral image classification and dimensionality reduction: An orthogonal subspace projection approach," IEEE Trans. Geosci. Remote Sens., vol. 32, pp. 779-785, Jul. 1994.
19. A. Ifarraguerri and C. I. Chang, "Unsupervised hyperspectral image analysis with projection pursuit," IEEE Trans. Geosci. Remote Sens., vol. 38, pp. 2529-2538, Nov. 2000.
20. G. F. Hughes, "On the mean accuracy of statistical pattern recognition," IEEE Trans. Inform. Theory, vol. 14, pp. 55-63, Jan. 1968.
21. K. P. Bennett and C. Campbell, "Support vector machines: hype or hallelujah?," SIGKDD Explorations Newsletter, vol.2, issue 2, 2000, pp. 1-13
22. S. Tadjudin and D. Landgrebe, "Covariance estimation for limited training samples," in Int. Geoscience and Remote Sensing Symposium, IEEE, (Seattle, WA), July 1998.
23. J. A. Gualtieri and R. F. Cromp, "Support vector machines for hyperspectral remote sensing classification," in the 27th AIPR Workshop, Advances in Computer Assisted Recognition, vol. 3584, R. J. Merisko, Ed. 1998, pp. 221-232.
24. J. A. Gualtieri, S. R. Chettri, R. F. Cromp, and L. F. Johnson, "Support vector machine classifiers as applied to AVIRIS data," in Summaries of the Eighth JPL Airborne Earth Science Workshop, February 8-11, 1999.
25. R. Fletcher, Practical Methods of Optimization, Second Edition, New York: Wiley, 2000.
26. T. M. Cover, "Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition," IEEE Transaction on Electronic Computers, vol. EC-14, 1965, pp. 326-334.
27. P. K. Varshney and M. K. Arora, Advanced Image Processing Techniques for Remotely Sensed Hyperspectral Data, Springer Verlag, To Appear in 2004.
28. U. Kreßel, "Pairwise classification and support vector machines," in Advances in Kernel Methods-Support Vector Learning, B Schölkopf, C. J. C. Burges, and A. J. Smola, Eds. Cambridge, MA: The MIT Press, 1999, pp. 255-268.
29. R. G. Congalton, "A review of assessing the accuracy of classifications of remotely sensed data," Remote Sensing of Environment, vol. 37, pp. 35-46, 1991.
30. O. L. Mangasarian and D. R. Musicant, "Lagrangian support vector machines," Tech. Rep 0006. Data Mining Institute, Computer Science Department, University of Wisconsin, Madison, Wisconsin, 2000.
31. http://dynamo.ecn.purdue.edu/~bieh/Multispec