Hyperspectral image classification based on regularized sparse representation

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

Volumn 7, Issue 6, 2014, Pages 2174-2182

  Yuan, Haoliang ^a   Tang, Yuan Yan ^a   Lu, Yang ^a   Yang, Lina ^a   Luo, Huiwu ^a  

^a UNIVERSITY OF MACAU   (Macao)

Author keywords

Classification; hyperspectral image (HSI); regularized sparse representation (RSR); spatial neighborhood

Indexed keywords

CLASSIFICATION (OF INFORMATION); SPECTROSCOPY;

CLASSIFICATION PERFORMANCE; OBJECTIVE FUNCTIONS; QUADRATIC CONSTRAINT; REGULARIZATION TERMS; SIMILARITY INFORMATIONS; SPARSE RECONSTRUCTION; SPARSE REPRESENTATION; SPATIAL NEIGHBORHOODS;

IMAGE CLASSIFICATION;

ALGORITHM; IMAGE CLASSIFICATION; PATTERN RECOGNITION; SPATIAL ANALYSIS; SPECTRAL ANALYSIS;

EID: 84905919348     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2014.2328601     Document Type: Article

References (27)

1. G. Hughes, "On the mean accuracy of statistical pattern recognizers," IEEE Trans. Inf. Theory, vol. 14, no. 1, pp. 55-63, Jan. 1968.
2. 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. (Pubitemid 40811944)
3. V. Vapnik, Statistical Learning Theory. Hoboken, NJ, USA: Wiley, 1998.
4. N. Cristianini and J. Shawe-Taylor, An Introduction to Support Vector Machines. Cambridge, U.K.: Cambridge Univ. Press, 2000.
5. G. Camps-Valls, L. Gomez-Chova, J. Munõz-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)
6. L. Bruzzone, M. Chi, and M. Marconcini, "A novel transductive SVM for the semisupervised classification of remote sensing images," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 11, pp. 3363-3373, Nov. 2006.
7. J. Muñoz-Marí, F. Bovolo, L. Gómez-Chova, L. Bruzzone, and G. Camp-Valls, "Semisupervised one-class support vector machines for classification of remote sensing data," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 8, pp. 3188-3197, Aug. 2010.
8. B. Kuo and D. Landgrebe, "Nonparametric weighted feature extraction for classification," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 5, pp. 1096-1105, May 2004.
9. J. Benediktsson, J. Palmason, and J. Sveinsson, "Classification of hyperspectral data from urban areas based on extended morphological profiles," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 3, pp. 480-491, Mar. 2005. (Pubitemid 40320270)
10. H. Huang and B. Kuo, "Double nearest proportion feature extraction for hyperspectral-image classification," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4034-4046, Nov. 2010.
11. W. Li, S. Prasad, J. Fowler, and L. Mann Bruce, "Locality preserving dimensionality reduction and classification for hyperspectral image analysis," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 4, pp. 1185-1198, Apr. 2012.
12. Y. Chen, N. Nasrabadi, and T. Tran, "Hyperspectral image classification using dictionary-based sparse representation," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 10, pp. 3973-3985, Oct. 2011.
13. Q. Haq, L. Tao, F. Sun, and S. Yang, "A fast and robust sparse approach for hyperspectral data classification using a few labeled samples," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 6, pp. 2287-2302, Jun. 2012.
14. Y. Qian and M. Ye, "Hyperspectral imagery restoration using nonlocal spectral-spatial structured sparse representation with noise estimation," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 2, pp. 499-515, Apr. 2013.
15. J. Liu, Z. Wu, Z. Wei, L. Xiao, and L. Sun, "Spatial-spectral kernel sparse representation for hyperspectral image classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 6, no. 6, pp. 2462-2471, Dec. 2013.
16. H. Zhang, J. Li, Y. Huang, and L. Zhang, "A nonlocal weighted joint sparse representation classification method for hyperspectral imagery," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., to be published, doi: 10.1109/JSTARS.2013.2264720.
17. D. Donoho, "For most large underdetermined systems of linear equations the minimal-norm solution is also the sparsest solution," Commun. Pure Appl. Math., vol. 59, no. 6, pp. 797-829, 2006. (Pubitemid 43667226)
18. R. Tibshirani, "Regression shrinkage and selection via the lasso," J. Roy. Stat. Soc. B, vol. 58, no. 1, pp. 267-288, 1996.
19. H. Lee, A. Battle, R. Raina, and A. Ng, "Efficient sparse coding algorithms," in Proc. Adv. Neural Inf. Process. Syst., 2007, pp. 801-808.
20. Y. Tarabalka, J. Benediktsson, J. Chanussot, and J. Tilton, "Multiple spectral-spatial classification approach for hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 48, no. 11, pp. 4122-4132, Nov. 2010.
21. K. Bernard, Y. Tarabalka, J. Angulo, J. Chanussot, and J. Benediktsson, "Spectral-spatial classification of hyperspectral data based on a stochastic minimum spanning forest approach," IEEE Trans. Image Process., vol. 21, no. 4, pp. 2008-2021, Apr. 2012.
22. P. Gurram and H. Kwon, "Contextual SVM using hilbert space embedding for hyperspectral classification," IEEE Geosci. Remote Sens. Lett., vol. 10, no. 5, pp. 1031-1035, Sep. 2013.
23. A. Plaza et al., "Recent advances in techniques for hyperspectral image processing," Remote Sens. Environ., vol. 113, no. 1, pp. S110-S122, 2010.
24. J. Tropp, A. Gilbert, and M. Strauss, "Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit," J. Signal Process., vol. 86, no. 3, pp. 572-588, 2006. (Pubitemid 43106572)
25. Hyperspectral Remote Sensing Images [Online]. Available: http://www. ehu.es/ccwintco/index.php/Hyperspectral-Remote-Sensing-Scenes
26. C. Chang and C. Lin, "LIBSVM: A library for support vector machines," ACM Trans. Intell. Syst. Technol., vol. 2, no. 3, pp. 27:1-27:27, 2011.
27. J. Li, H. Zhang, Y. Huang, and L. Zhang, "Hyperspectral image classification by nonlocal joint collaborative representation with a locally adaptive dictionary," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 6, pp. 3707-3719, Aug. 2013.