Non-local sparse unmixing for hyperspectral remote sensing imagery

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

Volumn 7, Issue 6, 2014, Pages 1889-1909

  Zhong, Yanfei ^a   Feng, Ruyi ^a   Zhang, Liangpei ^a  

^a Mapping and Remote Sensing   (China)

Author keywords

Hyperspectral remote sensing; non local; sparse unmixing; spatial information; spectral unmixing

Indexed keywords

CONSTRAINED OPTIMIZATION; REMOTE SENSING; SPECTROSCOPY;

HYPERSPECTRAL REMOTE SENSING; NONLOCAL; SPATIAL INFORMATIONS; SPECTRAL UNMIXING; UNMIXING;

ALGORITHMS;

ALGORITHM; IMAGE ANALYSIS; PIXEL; REMOTE SENSING; SPATIAL ANALYSIS; SPECTRAL ANALYSIS;

EID: 84905910179     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2013.2280063     Document Type: Article

References (36)

1. N. Keshava and J. F. Mustard, "Spectral unmixing," IEEE Signal Processing Mag., pp. 44-57, Jan. 2002. (Pubitemid 34237207)
2. M. D. Iordache, J. M. Bioucas-Dias, and A. Plaza, "Sparse unmixing of hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 6, pp. 2014-2039, 2011.
3. J. W. Boardman, F. A. Kruse, and R. O. Green, "Mapping target signatures via partial unmixing of AVIRIS data," in Proc. JPL Airborne Earth Sci. Workshop, 1995, pp. 23-26.
4. M. E. Winter, "N-FINDR: An algorithm for fast autonomous spectral end-member determination in hyperspectral data," in Proc. SPIE Imaging Spectrometry V, 2003, vol. 3753, pp. 266-275.
5. H. Ren and C.-I. Chang, "Automatic spectral target recognition in hyperspectral imagery," IEEE Trans. Aerosp. Electron. Syst., vol. 39, no. 4, pp. 1232-1249, Oct. 2003.
6. H. Li and L. Zhang, "A hybrid automatic endmember extraction algorithm based on a local window," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 11, pp. 4223-4238, Nov. 2011.
7. R. A. Neville, K. Staenz, T. Szeredi, J. Lefebvre, and P. Hauff, "Automatic endmember extraction from hyperspectral data for mineral exploration," in Proc. 21st Can. Symp. Remote Sens., 1999, pp. 21-24.
8. M. Berman, H. Kiiveri, R. Lagerstrom, A. Ernst, R. Dunne, and J. F. Huntington, "ICE: A statistical approach to identifying endmembers in hyperspectral images," IEEE Trans. Geosci. Remote Sens., vol. 42, no. 10, pp. 2085-2095, Oct. 2004.
9. J. M. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, D. Qian, P. Gader, and J. Chanussot, "Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. (JSTARS), vol. 5, no. 2, Apr. 2012.
10. J. Bioucas-Dias and M. Figueiredo, "Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing," in Proc. 2nd IEEE GRSS Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS), Reykjavik, Iceland, 2010.
11. M. D. Iordache, J. M. Bioucas-Dias, and A. Plaza, "Total variation spatial regularization for sparse hyperspectral unmixing," IEEE Trans. Geosci. Remote Sens., vol. 50, no. 11, pp. 4484-4502, Nov. 2012.
12. A. Buades, B. Coll, and J. M. Morel, "A review of image denoising algorithms, with a new one," Multiscale Model. Sim. (SIAM Interdisciplinary J.), vol. 4, no. 2, pp. 490-530, Jul. 2005. (Pubitemid 43890582)
13. A.Buades, B. Coll, and J. M. Morel, "A non-local algorithm for image denoising," in IEEE Computer Society Conf. Computer Vision and Pattern Recognition, 2005, CVPR, 2005, vol. 2, pp. 60-65.
14. Y. Qian, M. Ye, and Q. Wang, "Noise reduction of hyperspectral imagery using nonlocal sparse representation with spectral-spatial structure," in Proc. IEEE Int. Geosci. and Remote Sensing Symp., IGARSS, 2012, pp. 3467-3470.
15. Y. Qian, Y. Shen, M. Ye, and Q.Wang, "3-D nonlocal means filter with noise estimation for hyperspectral imagery denoising," in Proc. IEEE Int.Geosci. and Remote Sensing Symp., IGARSS, 2012, pp. 1345-1348.
16. G. Gilboa and S. J. Osher, "Nonlocal linear image regularization and supervised segmentation,"MultiscaleModel. Sim. (SIAMInterdisciplinary J.), vol. 6, no. 2, pp. 595-630, Jul. 2007.
17. M. Protter, M. Elad, and H. Takeda, "Generalizing the non-local-means to super-resolution reconstruction," IEEE Trans. Image Process., vol. 18, no. 1, pp. 36-51, Jan. 2009.
18. 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. (JSTARS), vol. 6, no. 2, pp. 499-515, Apr. 2013.
19. J. Li et al., "Classification for hyperspectral imagery based on non-local weighted joint sparsity model," in Proc. 4th Workshop Hyperspectral Image Signal Processing: Evolution in Remote Sensing, 2012, vol. 1, pp. 1-4.
20. M. Elad, Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. NewYork,NY,USA: Springer-Verlag, 2010.
21. A. M. Bruckstein, M. Elad, and M. Zibulevsky, "On the uniqueness of non-negative sparse solutions to underdetermined systems of equations," IEEE Trans. Inf. Theory, vol. 54, no. 11, pp. 4813-4820, Nov. 2008.
22. 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.
23. E. J. Candès and T. Tao, "Decoding by linear programming," IEEE Trans. Inf. Theory, vol. 51, no. 12, pp. 4203-4215, Dec. 2005. (Pubitemid 41800353)
24. E. J. Candès, J. Romberg, and T. Tao, "Stable signal recovery from incomplete and inaccurate measurements," Commun. Pure Appl. Math., vol. 59, no. 8, pp. 1207-1223, Aug. 2006. (Pubitemid 43988295)
25. E. Candès and T. Tao, "Decoding by linear programming," IEEE Trans. Inf. Theory, vol. 51, no. 12, pp. 4203-4215, Dec. 2005. (Pubitemid 41800353)
26. E. Candès and T. Tao, "Near-optimal signal recovery fromrandom projections: Universal encoding strategies," IEEE Trans. Inf. Theory, vol. 52, no. 22, pp. 5406-5424, Dec. 2006.
27. Y. C. Pati, R. Rezahfar, and P. S.Krishnaprasad, "Orthogonalmatching pursuit: Recursive function approximation with applications to wavelet decomposition," in Proc. 27th Annu. Asilomar Conf. Signals Systems and Computers, 1993, vol. 1, pp. 40-44.
28. S. Chen, D. Donoho, and M. Saunders, "Atomic decomposition by basis pursuit," SIAM Rev., vol. 43, no. 1, pp. 129-159, Feb. 2001. (Pubitemid 32406896)
29. D. Rogge, B. Rivard, J. Zhang, and J. Feng, "Iterative spectral unmixing for optimizing per-pixel endmember sets," IEEE Trans. Geosci. Remote Sens., vol. 44, no. 12, pp. 3725-3736, Dec. 2006.
30. G. Gilboa and S.Osher, "Nonlocal operators with applications to image processing," Multiscale Model. Simul., vol. 7, no. 3, pp. 1005-1028, Nov. 2008.
31. M. D. Iordache, "A sparse regression approach to hyperspectral unmixing," Ph.D. dissertation, Sch. Elect. Comput. Eng., IST, 2011.
32. J. Nascimento and J. M. Bioucas-Dias, "Vertex component analysis: A fast algorithm to unmix hyperspectral data," IEEE Trans. Geosci. Remote Sens., vol. 43, no. 4, pp. 898-910, Apr. 2005. (Pubitemid 40476033)
33. X. Xu, Y. Zhong, L. Zhang, and H. Zhang, "Sub-pixel mapping based on a MAP model with multiple shifted hyperspectral imagery," IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens. (JSTARS), vol. 6, no. 2, pp. 580-593, Apr. 2013.
34. J. Eckstein and D. Bertsekas, "On the Douglas-Rachford splitting method and the proximal point algorithm for maximal monotone operators," Math. Progr., vol. 5, pp. 293-318, Apr. 1992.
35. X. Zhang, M. Burger, X. Bresson, and S. Osher, "Bregmanized nonlocal regularization for deconvolution and sparse reconstruction," SIAM J. Imaging Sci., vol. 3, no. 3, pp. 253-276.
36. J. F. Cai, S. Osher, and Z. Shen, "Convergence of the Linearized Bregman Iteration for L1-Norm Minimization," UCLA, 2008, CAM Report 08-52.