Robust Collaborative Nonnegative Matrix Factorization for Hyperspectral Unmixing

IEEE Transactions on Geoscience and Remote Sensing

Volumn 54, Issue 10, 2016, Pages 6076-6090

  Li, Jun ^a   Bioucas Dias, Jose M ^b   Plaza, Antonio ^c   Liu, Lin ^a,d  

^a School of Geography and Planning   (China)

^b INSTITUTO DE TELECOMUNICAÇÕES   (Portugal)

^c UNIVERSITY OF EXTREMADURA   (Spain)

^d UNIVERSITY OF CINCINNATI   (United States)

Author keywords

Endmember extraction; hyperspectral imaging; robust collaborative nonnegative matrix factorization (R CoNMF); Spectral unmixing

Indexed keywords

ALGORITHMS; CHAINS; FACTORIZATION; PIXELS; SPECTROSCOPY;

COMPETITIVE PERFORMANCE; CONVENTIONAL METHODS; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL DATA; HYPERSPECTRAL UNMIXING; NONNEGATIVE MATRIX FACTORIZATION; PROPAGATION OF ERROR; SPECTRAL SIGNATURE;

MATRIX ALGEBRA;

ALGORITHM; DATA ACQUISITION; ERROR ANALYSIS; ESTIMATION METHOD; IMAGING METHOD; NUMERICAL METHOD; PIXEL; REMOTE SENSING; SPECTRAL ANALYSIS;

EID: 84978953872     PISSN: 01962892     EISSN: None     Source Type: Journal    
DOI: 10.1109/TGRS.2016.2580702     Document Type: Article

References (44)

1. A. Plaza, Q. Du, J. Bioucas-Dias, X. Jia, F. Kruse, "Foreword to the special issue on spectral unmixing of remotely sensed data, " IEEE Trans. Geosci. Remote Sens., vol. 49, no. 11, pp. 4103-4110, Nov. 2011.
2. N. Keshava and J. F. Mustard, "Spectral unmixing, " IEEE Signal Process. Mag., vol. 19, no. 1, pp. 44-57, Jan. 2002.
3. J. Bioucas-Dias et al., "Hyperspectral unmixing overview: Geometrical, statistical, sparse regression-based approaches, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 5, no. 2, pp. 354-379, Apr. 2012.
4. M. D. Craig, "Minimum-volume transforms for remotely sensed data, " IEEE Trans. Geosci. Remote Sens., vol. 32, no. 3, pp. 542-552, May 1994.
5. M. Winter, "N-FINDR: An algorithm for fast autonomous spectral endmember determination in hyperspectral data, " in Proc. SPIE, 1999, vol. 3753, pp. 266-270.
6. W.-K. Ma et al., "A signal processing perspective on hyperspectral unmixing: Insights from remote sensing, " IEEE Signal Process. Mag., vol. 31, no. 1, pp. 67-81, Jan. 2014.
7. J. M. Bioucas-Dias and J. Nascimento, "Hyperspectral unmixing based on mixtures of Dirichlet components, " IEEE Trans. Geosci. Remote Sens., vol. 50, no. 3, 863-878, Mar. 2011, in press.
8. O. Eches, N. Dobigeon, C. Mailhes, J.-Y. Tourneret, "Bayesian estimation of linear mixtures using the normal compositional model. Application to hyperspectral imagery, " IEEE Trans. Image Process., vol. 19, no. 6, pp. 1-11, Jun. 2010.
9. M.-D. Iordache, J. Bioucas-Dias, A. Plaza, "Sparse unmixing of hyperspectral data, " IEEE Trans. Geosci. Remote Sens., vol. 49, no. 6, pp. 2014-2039, Jun. 2011.
10. M.-D. Iordache, J. Bioucas-Dias, A. Plaza, "Total variation spatial regularization for sparse hyperspectral unmixing, " IEEE Trans. Geosci. Remote Sens., vol. 50, no. 11, pp. 4484-4502, Nov. 2012.
11. M.-D. Iordache, J. Bioucas-Dias, A. Plaza, "Collaborative sparse regression for hyperspectral unmixing, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 1, pp. 341-354, Jan. 2013.
12. M.-D. Iordache, J. Bioucas-Dias, A. Plaza, "MUSIC-CSR: Multiple signal classification collaborative sparse regression for hyperspectral unmixing, " IEEE Trans. Geosci. Remote Sens., vol. 52, no. 7, pp. 4364-4382, Jul. 2014.
13. M. Parente and A. Plaza, "Survey of geometric and statistical unmixing algorithms for hyperspectral images, " in Proc. IEEE 2nd WHISPERS, Reykjavik, Iceland, Jun. 14-16, 2010, pp. 1-4.
14. S. Jia and Y. Qian, "Constrained nonnegative matrix factorization for hyperspectral unmixing, " vol. 47, no. 1, pp. 161-173, Jan. 2009.
15. Y. Qian, S. Jia, J. Zhou, A. Robles-Kelly, "Hyperspectral unmixing via-1/2 sparsity-constrained nonnegative matrix factorization, " vol. 49, no. 11, pp. 4282-4297, Nov. 2011.
16. N. Keshava, "A survey of spectral unmixing algorithms, " Lincoln Lab. J., vol. 14, no. 1, pp. 55-78, 2003.
17. A. Zare and P. Gader, "Sparsity promoting iterated constrained endmember detection for hyperspectral imagery, " IEEE Geosci. Remote Sens. Lett., vol. 4, no. 3, pp. 446-450, Jul. 2007.
18. C.-I. Chang and Q. Du, "Estimation of number of spectrally distinct signal sources in hyperspectral imagery, " IEEE Trans. Geosci. Remote Sens., vol. 42, no. 3, pp. 608-619, Mar. 2004.
19. J. Harsanyi, W. Farrand, C.-I. Chang, "Determining the number and identity of spectral endmembers: An integrated approach using Neyman-Pearson eigenthresholding and iterative constrained RMS error minimization, " in Proc. Thematic Conf. Geol. Remote Sens., 1993, vol. 1, pp. 1-10.
20. J. M. Bioucas-Dias and J. M. P. Nascimento, "Hyperspectral subspace identification, " IEEE Trans. Geosci. Remote Sens., vol. 46, no. 8, pp. 2435-2445, Aug. 2008.
21. B. Luo, J. Chanussot, S. Doute, L. Zhang, "Empirical automatic estimation of the number of endmembers in hyperspectral images, " IEEE Geosci. Remote Sens. Lett., vol. 10, no. 1, pp. 24-28, Jan. 2013.
22. C. Andreou and V. Karathanassi, "Estimation of the number of endmembers using robust outlier detection method, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 1, pp. 247-256, Jan. 2014.
23. A. Ambikapathi, T.-H. Chan, C.-Y. Chi, K. Keizer, "Hyperspectral data geometry-based estimation of number of endmembers using p-normbased pure pixel identification algorithm, " IEEE Trans. Geosci. Remote Sens., vol. 51, no. 5, pp. 2753-2769, May 2013.
24. R. Heylen, M. Parente, P. Gader, "A review of nonlinear hyperspectral unmixing methods, " IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens., vol. 7, no. 6, pp. 1844-1868, Jun. 2014.
25. L. Miao and H. Qi, "Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization, " IEEE Trans. Geosci. Remote Sens., vol. 45, no. 3, pp. 765-777, Mar. 2007.
26. J. Li, A. Agathos, D. Zaharie, J. M. B. Dias, A. Plaza, X. Li, "Minimum volume simplex analysis: A fast algorithm for hyperspectral unmixing, " IEEE Trans. Geosci. Remote Sens., vol. 53, no. 9, pp. 5067-5082, Sep. 2015.
27. J. Bioucas-Dias, "A variable splitting augmented Lagrangian approach to linear spectral unmixing, " in Proc. 1st IEEE WHISPERS, Grenoble, France, Aug. 26-28, 2009, pp. 1-4.
28. T. Chan, C. Chi, Y. Huang, W. Ma, "Convex analysis based minimumvolume enclosing simplex algorithm for hyperspectral unmixing, " IEEE Trans. Signal Process., vol. 57, no. 11, pp. 4418-4432, Nov. 2009.
29. T.-H. Chan, C.-Y. Chi, Y.-M. Huang, W.-K. Ma, "A convex analysisbased minimum-volume enclosing simplex algorithm for hyperspectral unmixing, " IEEE Trans. Signal Process., vol. 57, no. 11, pp. 4418-4432, Nov. 2009.
30. A. Huck, M. Guillaume, J. Blanc-Talon, "Minimum dispersion constrained nonnegative matrix factorization to unmix hyperspectral data, " IEEE Trans. Geosci. Remote Sens., vol. 48, no. 6, pp. 2590-2602, Jun. 2010.
31. A. Ambikapathi, T.-H. Chan, W.-K. Ma, C.-Y. Chi, "Chanceconstrained robust minimum-volume enclosing simplex algorithm for hyperspectral unmixing, " IEEE Trans. Geosci. Remote Sens., vol. 49, no. 11, pp. 4194-4209, Nov. 2011.
32. J. Tropp, "Algorithms for simultaneous sparse approximation. Part II:Convex relaxation, " Signal Process., vol. 86, no. 3, pp. 589-602, Mar. 2006.
33. Y. Eldar and H. Rauhut, "Average case analysis of multichannel sparse recovery using convex relaxation, " IEEE Trans. Image Process., vol. 56, no. 1, pp. 505-519, Jan. 2010.
34. M. Fornasier and H. Rauhut, "Recovery algorithms for vector-valued data with joint sparsity constraints, " SIAM J. Numerical Anal., vol. 46, no. 2, pp. 577-613, 2008.
35. H. Attouch, J. Bolte, P. Redont, A. Soubeyran, "Proximal alternating minimization and projection methods for nonconvex problems: An approach based on the Kurdyka-Lojasiewicz inequality, " Math. Oper. Res., vol. 35, no. 2, pp. 438-457, May 2010.
36. L. Grippof and M. Sciandrone, "Globally convergent block-coordinate techniques for unconstrained optimization, " Optim. Methods Softw., vol. 10, no. 4, pp. 587-637, 1999.
37. J. Bioucas-Dias and M. Figueiredo, "Alternating direction algorithms for constrained sparse regression: Application to hyperspectral unmixing, " in Proc. 2nd WHISPERS, Jun. 2010, pp. 1-4.
38. A. Green, M. Berman, P. Switzer, M. Craig, "A transformation for ordering multispectral data in terms of image quality with implications for noise removal, " IEEE Trans. Geosci. Remote Sens., vol. 26, no. 1, pp. 65-74, Jan. 1988.
39. 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. 2005.
40. Y. Xu and W. Yin, "A block coordinate descent method for regularized multiconvex optimization with applications to nonnegative tensor factorization and completion, " SIAM J. Imaging Sci., vol. 6, no. 3, pp. 1758-1789, 2013.
41. X. Jia and J. A. Richards, "Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification, " IEEE Trans. Geosci. Remote Sens., vol. 37, no. 1, pp. 538-542, Jan. 1999.
42. G. Swayze, R. Clark, S. Sutley, A. Gallagher, "AVIRIS mineral mapping at Cuprite, Nevada, " in Proc. Summaries 3rd Annu. JPL Airborne Geosci. Workshop, 1992, vol. 1, pp. 47-49.
43. S. Robila and L. Maciak, "Considerations on parallelizing nonnegative matrix factorization for hyperspectral data unmixing, " IEEE Geosci. Remote Sens. Lett., vol. 6, no. 1, pp. 57-61, Jan. 2009.
44. Y.-X. Wang and Y.-J. Zhang, "Nonnegative matrix factorization: A comprehensive review, " IEEE Trans. Knowl. Data Eng., vol. 25, no. 6, pp. 1336-1353, Jun. 2013.