An endmember dissimilarity constrained non-negative matrix factorization method for hyperspectral unmixing

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

Volumn 6, Issue 2, 2013, Pages 554-569

  Wang, Nan ^a   Du, Bo ^a   Zhang, Liangpei ^a  

^a WUHAN UNIVERSITY   (China)

Author keywords

Hyperspectral imagery; linear mixture model; non negative matrix factorization; spectral unmixing

Indexed keywords

ENDMEMBERS; HYPER-SPECTRAL IMAGERIES; HYPERSPECTRAL UNMIXING; LINEAR MIXTURE MODELS; NONCONVEX PROBLEM; NONNEGATIVE MATRIX FACTORIZATION; SPECTRAL UNMIXING; UNMIXING;

SPECTROSCOPY;

FACTORIZATION;

ALGORITHM; DATA SET; EXPERIMENTAL STUDY; IMAGE ANALYSIS; PERFORMANCE ASSESSMENT; REMOTE SENSING; SPECTRAL RESOLUTION;

EID: 84877927996     PISSN: 19391404     EISSN: 21511535     Source Type: Journal    
DOI: 10.1109/JSTARS.2013.2242255     Document Type: Article

References (40)

1. C. -I Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification. New York: Plenum, 2003.
2. B. Du, L. Zhang, L. F. Zhang, T. Chen, and K. Wu, "A discriminative manifold learning based dimension reduction method for hyperspectral classification," Int. J. Fuzzy Syst. , vol. 14, no. 2, pp. 272-277, Jun. 2012.
3. N. Keshava and J. F. Mustard, "Spectral unmixing," IEEE Signal Process. Mag. , vol. 19, no. 1, pp. 44-57, Jan. 2002. (Pubitemid 34237207)
4. J. Li and J. M. Bioucas-Dias, "Minimum volume simplex analysis: A fast algorithm to unmix hyperspectral data," in Proc. IGARSS, Boston, MA, Jul. 2008, vol. 3, pp. 250-253.
5. 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. , vol. 5, no. 2, pp. 354-379, 2012.
6. M. E. Winter, "N-FINDR: An algorithm for fast autonomous spectral endmember determination in hyperspectral data," in Proc. SPIE Conf. Imaging Spectrometry V, 1999, vol. 3753, pp. 266-275.
7. C. -I Chang, C. -C. Wu, W. Liu, and Y. -C. Ouyang, "A new growing method for simplex-based endmember extraction algorithm," IEEE Trans. Geosci. Remote Sens. , vol. 44, no. 10, pp. 2804-2819, Oct. 2006.
8. J. Boardman, "Automating spectral unmixing of AVIRIS data using convex geometry concepts," in Proc. AVIRIS Workshop, 1993, vol. 1, pp. 11-14, JPL Pub. 93-26.
9. J. Nascimento and J. 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. 2002. (Pubitemid 40476033)
10. A. Plaza, P. Martinez, R. Perez, and J. Plaza, "Spatial/spectral endmember extraction by multidimensional morphological operations," IEEE Trans. Geosci. Remote Sens. , vol. 40, no. 9, pp. 2025-2041, Sep. 2002. (Pubitemid 35458399)
11. J. M. Bioucas-Dias, "A variable splitting augmented Lagrangian approach to linear spectral unmixing," in 1st Workshop Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, WHISPERS '09, 2009, pp. 1-4.
12. A. Hyvarinen, J. Karhunen, and E. Oja, Independent Component Analysis. New York: Wiley, 2001.
13. D. D. Lee and H. S. Seung, "Algorithms for non-negative matrix factorization," Adv. Neural Inf. Process. Syst. , vol. 13, pp. 556-562, 2000.
14. S. Moussaoui, H. Hauksdottir, F. Schmidt, C. Jutten, J. Chanussot, D. Brie, S. Doute, and J. A. Benediktsson, "On the decomposition ofMars hyperspectral data by ICA and Bayesian positive source separation," Neurocomputing, vol. 71, no. 10-12, pp. 2194-2208, Jun. 2008.
15. D. Donoho and V. Stodden, "When does non-negative matrix factorization give a correct decomposition into parts,," in Advances in Neural Information Processing Systems 16: Proc. 2003 Conf. , 2003.
16. G. X. Zhou, S. L. Xie, S. X. Ding, J. M. Yang, and J. Zhang, "Blind spectral unmixing based on sparse nonnegative matrix factorization," IEEE Trans. Image Process. , vol. 20, pp. 1112-1125, Apr. 2011.
17. A. Huck, M. Guillaume, and J. Blanc-Talon, "Minimum dispersion constrained nonnegative matrix factorization to unmix hyperspectral data," IEEE Trans. Geosci. Remote Sens. , vol. 48, pp. 2590-2602, 2010.
18. L. Miao and H. Qi, "Endmember extraction from highly mixed data usingminimumvolume constrained nonnegativematrix factorization," IEEE Trans. Geosci. Remote Sens. , vol. 45, no. 3, pp. 765-777, Mar. 2007. (Pubitemid 46375748)
19. S. Jia and Y. T. Qian, "Constrained nonnegative matrix factorization for hyperspectral unmixing," IEEE Trans. Geosci. Remote Sens. , vol. 47, pp. 161-173, Jan. 2009.
20. X. S. Liu, W. Xia, B. Wang, and L. M. Zhang, "An approach based on constrained nonnegative matrix factorization to unmix hyperspectral data," IEEE Trans. Geosci. Remote Sens. , vol. 49, pp. 757-772, Feb. 2011.
21. M. D. Craig, "Minimum-volume transforms for remotely sensed data," IEEE Trans. Geosci. Remote Sens. , vol. 32, no. 3, pp. 542-552, May 1994.
22. T. H. Chan, W. K. Ma, A. Ambikapathi, and C. Y. Chi, "A simplex volume maximization framework for hyperspectral endmember extraction," IEEE Trans. Geosci. Remote Sens. , vol. 49, pp. 4177-4193, 2011.
23. T. H. Chan, C. Y. Chi, Y. M. Huang, and W. K. Ma, "A convex analysis-based minimum-volume enclosing simplex algorithm for hyperspectral unmixing," IEEE Trans. Signal Process. , vol. 57, pp. 4418-4432, 2009.
24. P. Paatero and U. Tapper, "Positive matrix factorization: A non-negative factor model with optimal utilization of error," Environmetrics, vol. 5, pp. 111-126, 1994.
25. C. J. Lin, "Projected gradient methods for non-negative matrix factorization," Neural Comput. , vol. 19, no. 10, pp. 2756-2779, Oct. 2007.
26. P. O. Hoyer, "Non-negative matrix factorization with sparseness constraints," J. Mach. Learn. Res. , vol. 5, pp. 1457-1469, 2004.
27. M. W. Berry, M. Browne, A. N. Langville, and V. P. Pauca, "Algorithms and applications for approximate nonnegative matrix factorization," Comput. Stat. Data An. , vol. 52, pp. 155-173, 2007. (Pubitemid 47331703)
28. C. Deng et al. , "Graph regularized nonnegative matrix factorization for data representation," IEEE Trans. Pattern Anal. Mach. Intell. , vol. 33, pp. 1548-1560, 2011.
29. N. Y. Guan et al. , "Manifold regularized discriminative nonnegative matrix factorization with fast gradient descent," IEEE Trans. Image Process. , vol. 20, pp. 2030-2048, Jul. 2011.
30. A. Plaza and C. -I Chang, "Impact of initialization on design of endmember extraction algorithms," IEEE Trans. Geosci. Remote Sens. , vol. 44, no. 11, pp. 3397-3407, Nov. 2006. (Pubitemid 44711681)
31. C. -Y. Liou and K. O. Yang, "Unsupervised classification of remote sensing imagery with non-negative matrix factorization," in Proc. ICONIP, 2005, pp. 280-285.
32. S. Wild, J. Curry, and A. Dougherty, "Improving non-negative matrix factorization through structured initialization," Pattern Recognit. , vol. 37, no. 11, pp. 2217-2232, Nov. 2004. (Pubitemid 40134553)
33. C. Boutsidis and E. Gallopoulos, "SVD based initialization: A head start for nonnegative matrix factorization," Pattern Recognit. , vol. 41, no. 4, pp. 1350-1362, Apr. 2008. (Pubitemid 350212975)
34. I. C. Chein, "Spectral information divergence for hyperspectral image analysis," in Proc. IGARSS '99, IEEE 1999 Int. , vol. 1, pp. 509-511.
35. A. Plaza, P. Martinez, R. Perez, and J. Plaza, "A quantitative and comparative analysis of endmember extraction algorithms from hyperspectral data," IEEE Trans. Geosci. Remote Sens. , vol. 42, no. 3, pp. 650-663, Mar. 2004.
36. D. Landgrebe, Multispectral Data Analysis: A Signal Theory Perspective. MultiSpec. Sch. Elect. Comput. Eng. , Purdue Univ. , West Lafayette, IN, USA, 1998, pp. 56-89.
37. R. N. Clark and G. A. Swayze, "Evolution in imaging spectroscopy analysis and sensor signal-to-noise:An examination of how farwe have come," in Proc. 6th Annu. JPL Airborne Earth Sci. Workshop, Mar. 1996, pp. 49-53.
38. X. Tao, B. Wang, and L. Zhang, "Orthogonal bases approach for decomposition of mixed pixels for hyperspectral imagery," IEEE Geosci. Remote Sens. Lett. , vol. 6, no. 2, pp. 219-223, Apr. 2009.
39. D. C. Heinz and I. C. Chein, "Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery," IEEE Trans. Geosci. Remote Sens. , vol. 39, pp. 529-545, 2001. (Pubitemid 32400422)
40. B. Du and L. Zhang, "Random selection based anomaly detector for hyperspectral imagery," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 5, pp. 1578-1589, May 2011.