Hyperspectral Image Classification with Robust Sparse Representation

IEEE Geoscience and Remote Sensing Letters

Volumn 13, Issue 5, 2016, Pages 641-645

  Li, Chang ^a   Ma, Yong ^b   Mei, Xiaoguang ^b   Liu, Chengyin ^a   Ma, Jiayi ^b  

^a HUAZHONG UNIVERSITY OF SCIENCE AND TECHNOLOGY   (China)

^b WUHAN UNIVERSITY   (China)

Author keywords

Hyperspectral classification; joint RSRC (JRSRC); outliers; robust SRC (RSRC); Sparse representation based classification (SRC)

Indexed keywords

INDEPENDENT COMPONENT ANALYSIS; PIXELS; SAMPLING; SPECTROSCOPY; STATISTICS;

HYPER-SPECTRAL CLASSIFICATION; HYPER-SPECTRAL IMAGERIES; LINEAR COMBINATIONS; ORTHOGONAL MATCHING PURSUIT; OUTLIERS; ROBUST SRC (RSRC); SPARSE REPRESENTATION; SPARSE REPRESENTATION BASED CLASSIFICATIONS;

IMAGE CLASSIFICATION;

EID: 84977943920     PISSN: 1545598X     EISSN: None     Source Type: Journal    
DOI: 10.1109/LGRS.2016.2532380     Document Type: Article

References (24)

1. M. Cui and S. Prasad, "Class-dependent sparse representation classifier for robust hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2683-2695, May 2015.
2. C. Li, Y. Ma, J. Huang, X. Mei, J. Ma, "Hyperspectral image denoising using the robust low-rank tensor recovery," J. Opt. Soc. Amer. A, vol. 32, no. 9, pp. 1604-1612, Sep. 2015.
3. W. Li, E. W. Tramel, S. Prasad, J. E. Fowler, "Nearest regularized subspace for hyperspectral classification," IEEE Trans. Geosci. Remote Sens., vol. 52, no. 1, pp. 477-489, Jan. 2014.
4. Y. Li, C. Tao, Y. Tan, K. Shang, J. Tian, "Unsupervised multilayer feature learning for satellite image scene classification," IEEE Geosci. Remote Sens. Lett., vol. 13, no. 2, pp. 157-161, Feb. 2016.
5. L. Ma, M. M. Crawford, X. Yang, Y. Guo, "Local-manifold-learningbased graph construction for semisupervised hyperspectral image classification," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 5, pp. 2832-2844, May 2015.
6. L. Ma, X. Zhang, X. Yu, D. Luo, "Spatial regularized local manifold learning for classification of hyperspectral images," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 9, no. 2, pp. 609-624, Feb. 2016.
7. 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.
8. J. Wright, A. Y. Yang, A. Ganesh, S. S. Sastry, Y. Ma, "Robust face recognition via sparse representation," IEEE Trans. Pattern Anal. Mach. Intell., vol. 31, no. 2, pp. 210-227, Feb. 2009.
9. M. Yang, L. Zhang, J. Yang, D. Zhang, "Robust sparse coding for face recognition," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2011, pp. 625-632.
10. J. Ma, J. Zhao, J. Tian, X. Bai, Z. Tu, "Regularized vector field learning with sparse approximation for mismatch removal," Pattern Recognit., vol. 46, no. 12, pp. 3519-3532, Dec. 2013.
11. J. Ma et al., "Robust feature matching for remote sensing image registration via locally linear transforming," IEEE Trans. Geosci. Remote Sens., vol. 53, no. 12, pp. 6469-6481, Dec. 2015.
12. J. Ma, J. Zhao, A. L. Yuille, "Non-rigid point set registration by preserving global and local structures," IEEE Trans. Image Process., vol. 25, no. 1, pp. 53-64, Jan. 2016.
13. Y. Chen, N. M. Nasrabadi, 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.
14. Y. Chen, N. M. Nasrabadi, T. D. Tran, "Hyperspectral image classification via kernel sparse representation," IEEE Trans. Geosci. Remote Sens., vol. 51, no. 1, pp. 217-231, Jan. 2013.
15. J. Liu, Z. Wu, Z. Wei, L. Xiao, 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. L. Zhang, M. Yang, X. Feng, "Sparse representation or collaborative representation: Which helps face recognition?" in Proc. IEEE Int. Conf. Comput. Vis., 2011, pp. 471-478.
17. W. Li and Q. Du, "Gabor-filtering-based nearest regularized subspace for hyperspectral image classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 4, pp. 1012-1022, Apr. 2014.
18. W. Li, Q. Du, M. Xiong, "Kernel collaborative representation with Tikhonov regularization for hyperspectral image classification," IEEE Geosci. Remote Sens. Lett., vol. 12, no. 1, pp. 48-52, Jan. 2015.
19. D.Wang, H. Lu, M.-H. Yang, "Kernel collaborative face recognition," Pattern Recognit., vol. 48, no. 10, pp. 3025-3037, Oct. 2015.
20. E. Elhamifar and R. Vidal, "Robust classification using structured sparse representation," in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., 2011, pp. 1873-1879.
21. R. Tibshirani, "Regression shrinkage and selection via the lasso: A retrospective," J. R. Statist. Soc., Ser. B, vol. 73, no. 3, pp. 273-282, 2011.
22. Y. Fang et al., "Dimensionality reduction of hyperspectral images based on robust spatial information using locally linear embedding," IEEE Geosci. Remote Sens. Lett., vol. 11, no. 10, pp. 1712-1716, Oct. 2014.
23. C. Chen et al., "Spectral-spatial preprocessing using multihypothesis prediction for noise-robust hyperspectral image classification," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 4, pp. 1047-1059, Apr. 2014.
24. P. Du, Z. Xue, J. Li, A. Plaza, "Learning discriminative sparse representations for hyperspectral image classification," IEEE J. Sel. Topics Signal Process., vol. 9, no. 6, pp. 1089-1104, Sep. 2015.