Exploiting sparsity in hyperspectral image classification via graphical models

IEEE Geoscience and Remote Sensing Letters

Volumn 10, Issue 3, 2013, Pages 505-509

  Srinivas, Umamahesh ^a   Chen, Yi ^b   Monga, Vishal ^a   Nasrabadi, Nasser M ^c   Tran, Trac D ^b  

^a The Pennsylvania State University   (United States)

^b Johns Hopkins University   (United States)

^c U S ARMY RESEARCH LABORATORY   (United States)

Author keywords

Classification; hyperspectral imagery; joint sparsity model; probabilistic graphical models; sparse representation; spatial correlation

Indexed keywords

HYPERSPECTRAL IMAGERY; JOINT SPARSITY; PROBABILISTIC GRAPHICAL MODELS; SPARSE REPRESENTATION; SPATIAL CORRELATIONS;

CLASSIFICATION (OF INFORMATION); GRAPHIC METHODS; INDEPENDENT COMPONENT ANALYSIS; REMOTE SENSING; SPECTROSCOPY; SPEECH RECOGNITION; TREES (MATHEMATICS);

PIXELS;

EID: 84870541423     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2012.2211858     Document Type: Article

References (23)

1. M. Borengasser, W. S. Hungate, and R. Watkins, Hyperspectral Remote Sensing-Principles and Applications. Boca Raton, FL: CRC Press, 2008.
2. V. N. Vapnik, The Nature of Statistical Learning Theory. Berlin, Germany: Springer-Verlag, 1995.
3. 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.
4. G. Camps-Valls, L. Gomez-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)
5. F. Bovolo, L. Bruzzone, and M. Marconcini, "A novel context-sensitive SVM for classification of remote sensing images," in Proc. IEEE Int. Geosci. Remote Sens. Symp., Denver, Jul. 2006, pp. 2498-2501. (Pubitemid 47516069)
6. Y. Tarabalka, J. A. Benediktsson, and J. Chanussot, "Spectral- spatial classification of hyperspectral imagery based on partitional clustering techniques," IEEE Trans. Geosci. Remote Sens., vol. 47, no. 8, pp. 2973-2987, Aug. 2009.
7. J. Li, J. M. Bioucas-Dias, and A. Plaza, "Spectral-spatial hyperspectral image segmentation using subspace multinomial logistic regression and Markov random fields," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 3, pp. 809-823, Mar. 2012.
8. Y. Chen, N. Nasrabadi, and T. D. Tran, "Hyperspectral image classification using dictionary-based sparse representation," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 10, pp. 3973-3985, Oct. 2011.
9. V. Cevher, P. Indyk, L. Carin, and R. Baraniuk, "Sparse signal acquisition and recovery with graphical models," IEEE Signal Process. Mag., vol. 27, no. 6, pp. 92-103, Nov. 2010.
10. V. Y. F. Tan, S. Sanghavi, J. W. Fisher, and A. S. Willsky, "Learning graphical models for hypothesis testing and classification," IEEE Trans. Signal Process., vol. 58, no. 11, pp. 5481-5495, Nov. 2010.
11. Y. Freund and R. E. Schapire, "A short introduction to boosting," J. Jpn. Soc. Artif. Intell., vol. 14, no. 5, pp. 771-780, Sep. 1999.
12. T. Downs and A. Tang, "Boosting the tree augmented naive Bayes classifier," in Proc. Intell. Data Eng. Automated Learn., Lecture Notes in Computer Science, 2004, vol. 3177, pp. 708-713.
13. J. Wright, A. Y. Yang, A. Ganesh, S. Sastry, and Y. Ma, "Robust face recognition via sparse representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210-227, Feb. 2009.
14. A. M. Bruckstein, D. L. Donoho, and M. Elad, "From sparse solutions of systems of equations to sparse modeling of signals and images," SIAM Rev., vol. 51, no. 1, pp. 34-81, Feb. 2009.
15. J. A. Tropp, A. C. Gilbert, andM. J. Strauss, "Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit," Signal Process., vol. 86, no. 3, pp. 572-588, Mar. 2006. (Pubitemid 43106572)
16. 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.
17. C. Bishop, Pattern Recognition and Machine Learning. New York: Springer-Verlag, 2006.
18. M. J. Wainwright and M. I. Jordan, Graphical Models, Exponential Families, and Variational Inference. Hanover, MA: Now Publ. Inc., 2008.
19. C. K. Chow and C. N. Liu, "Approximating discrete probability distributions with dependence trees," IEEE Trans. Inf. Theory, vol. IT-14, no. 3, pp. 462-467, May 1968.
20. G. F. Cooper, "The computational complexity of probabilistic inference using Bayesian belief networks," Artif. Intell., vol. 42, no. 2/3, pp. 393-405, Mar. 1990.
21. U. Srinivas, V. Monga, and R. G. Raj, "Automatic target recognition using discriminative graphical models," in Proc. IEEE Int. Conf. Image Process., 2011, pp. 33-36.
22. J. A. Gualtieri and R. F. Cromp, "Support vector machines for hyperspectral remote sensing classification," in Proc. SPIE, Jan. 1998, vol. 3584, pp. 221-232.
23. AVIRIS NW Indiana's Indian Pines 1992 Data Set. [Online]. Available: http://cobweb.ecn.purdue.edu/biehl/MultiSpec/documentation.html