Structured priors for sparse-representation-based hyperspectral image classification

IEEE Geoscience and Remote Sensing Letters

Volumn 11, Issue 7, 2014, Pages 1235-1239

  Sun, Xiaoxia ^a   Qu, Qing ^a   Nasrabadi, Nasser M ^b   Tran, Trac D ^a  

^a Whiting School of Engineering   (United States)

^b U S ARMY RESEARCH LABORATORY   (United States)

Author keywords

Classification; hyperspectral image (HSI); sparse representation; structured priors

Indexed keywords

CLASSIFICATION (OF INFORMATION); IMAGE CLASSIFICATION; SPECTROSCOPY;

HYPERSPECTRAL IMAGE CLASSIFICATION; HYPERSPECTRAL IMAGES; LINEAR COMBINATIONS; PIXELWISE CLASSIFICATION; SPARSE REPRESENTATION; SPATIAL DEPENDENCE; STRUCTURED PRIORS; STRUCTURED SPARSITIES;

PIXELS;

EID: 84897025270     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2013.2290531     Document Type: Article

References (21)

1. A. Plaza, J. Benediktsson, J. Boardman, J. Brazile, L. Bruzzone, G. Camps-Valls, J. Chanussot, M. Fauvel, P. Gamba, A. Gualtieri, M. Marconcini, J. Tiltoni, and G. Trianni, Recent advances in techniques for hyperspectral image processing, Remote Sens. Environ., vol. 113, no. S1, pp. S110-S122, Sep. 2009
2. G. Camps-Valls, L. Gomez-Chova, J. Muoz-Mar, J. Vila-Francs, 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)
3. L. Gomez-Chova, G. Camps-Valls, J. Muoz-Mar, and J. Calpe-Maravilla, Semi-supervised image classification with Laplacian support vector machines, IEEE Geosci. Remote Sens. Lett., vol. 5, no. 3, pp. 336-340, Jul. 2008
4. J. Zhu, S. Rosset, T. Hastie, and R. Tibshirani, 1-norm support vector machines, in Proc. NIPS, Dec. 2003, vol. 16, pp. 16-23
5. J. Wright, A. 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
6. J. Wright, J.Mairal, G. Sapiro, T. S. Huang, and S. Yan, Sparse representation for computer vision and pattern recognition, Proc. IEEE, vol. 98, no. 6, pp. 1031-1044, Jun. 2010
7. Y. Chen, M. 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
8. 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
9. R. Ji, Y. Gao, R. Hong, Q. Liu, D. Tao, and X. Li, Spectral-spatial constraint hyperspectral image classification, IEEE Trans. Geosci. Remote Sens., vol. 52, no. 3, pp. 1811-1824, Mar. 2014
10. M. Iordache, J. Bioucas-Dias, and A. Plaza, Sparse unmixing of hyperspectral data, IEEE Geosci. Remote Sens., vol. 49, no. 6, pp. 2014-2039, Jun. 2011
11. J. Tropp, A. Gilbert, and M. Strauss, Algorithms for simultaneous sparse approximation. Part I: Greedy pursuit, Signal Process., vol. 54, no. 12, pp. 4634-4643, Dec. 2006
12. E. Berg and M. Friedlander, Joint-sparse recovery from multiple measurements, IEEE Trans. Inf. Theory, vol. 56, no. 5, pp. 2516-2527, Apr. 2010
13. S. Gao, I. Tsang, and L. Chia, Laplacian sparse coding, hypergraph Laplacian sparse coding, and applications, IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 1, pp. 92-104, Jan. 2013
14. G. Liu, Z. Lin, S. Yan, J. Sun, Y. Yu, and Y. Ma, Robust recovery of subspace structures by low-rank representation, IEEE Trans. Pattern Anal. Mach. Intell., vol. 35, no. 1, pp. 171-184, Jan. 2013
15. A. Rakotomamonjy, Surveying and comparing simultaneous sparse approximation (or group-Lasso) algorithms, Signal Process., vol. 91, no. 7, pp. 1505-1526, Jul. 2011
16. S. Kim and E. Xing, Tree-guided group lasso for multi-task regression with structured sparsity, in Proc. ICML, 2010, pp. 543-550
17. P. Sprechmann, I. Ramirez, G. Sapiro, and Y. Eldar, C-HiLasso: A collaborative hierarchical sparse modeling framework, IEEE Trans. Signal Process., vol. 59, no. 9, pp. 4183-4198, Sep. 2011
18. Y. Qian, M. Ye, and J. Zhou, Hyperspectral image classification based on structured sparse logistic regression and three-dimensional wavelet texture features, IEEE Trans. Geosci. Remote Sens., vol. 51, no. 4, pp. 2276-2291, Apr. 2013
19. E. Elhamifar and R. Vidal, Sparse subspace clustering, in Proc. IEEE CVPR, Jun. 2009, pp. 2790-2797
20. S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed optimization and statistical learning via the alternating direction method of multipliers, Found. Trends Mach. Learn., vol. 3, no. 1, pp. 1-122, Jan. 2010
21. S. Wright, R. Nowak, and M. Figueiredo, Sparse reconstruction by separable approximation, IEEE Trans. Signal Process., vol. 57, no. 7, pp. 2479-2493, Jul. 2009.