Hyperspectral image unmixing via bilinear generalized approximate message passing

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 8743, Issue , 2013, Pages

  Vila, Jeremy ^c   Schnite, Philip ^a   Meola, Joseph ^b  

^a The Ohio State University   (United States)

^b AIR FORCE RESEARCH LABORATORY   (United States)

^c NONE

Author keywords

Approximate message passing; Hyperspectral image unmixing; Loopy belief propagation

Indexed keywords

BELIEF PROPAGATION; MAXIMUM PRINCIPLE; REMOTE SENSING; SPECTROSCOPY;

APPROXIMATE MESSAGE PASSING; ELECTROMAGNETICS; ENDMEMBERS; HYPERSPECTRAL; HYPERSPECTRAL IMAGE UNMIXING; HYPERSPECTRAL UNMIXING; LOOPY BELIEF PROPAGATION; MESSAGE-PASSING; MESSAGE-PASSING ALGORITHM; UNMIXING;

FACTORIZATION;

EID: 84881144384     PISSN: 0277786X     EISSN: 1996756X     Source Type: Conference Proceeding    
DOI: 10.1117/12.2015859     Document Type: Conference Paper

References (26)

1. Johnson, P. E., Smith, M. O., Taylor-George, S., and Adams, J. B., "A semiempirical method for analysis of the reflectance spectra of binary mineral mixtures, " Journal of Geophysical Research 88, 3557-3561 (1983).
2. Bioucas-Dias, J., Plaza, A., Dobigeon, N., Parente, M., Du, Q., Gader, P., and Chanussot, J., "Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches, " Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of 5(2), 354-379 (2012).
3. Heinz, D. and Chang, C.-I., "Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery, " Geoscience and Remote Sensing, IEEE Transactions on 39(3), 529-545 (2001).
4. Vila, J. and Schniter, P., "Generalized approximate message passing with simplex constraints, " in [in preparation], (2013).
5. Winter, M. E., "N-FINDR: an algorithm for fast autonomous spectral end-member determination in hyper- spectral data, " Proc. SPIE, 266-275 (1999).
6. Nascimento, J. and Bioucas Dias, J., "Vertex component analysis: A fast algorithm to unmix hyperspectral data, " Geoscience and Remote Sensing, IEEE Transactions on 43(4), 898-910 (2005).
7. Gillis, N. and Vavasis, S. A., "Fast and robust recursive algorithms for separable nonnegative matrix fac- torization, " arXiv:1208.1237v2 (2012).
8. Heylen, R., Burazerovic, D., and Scheunders, P., "Fully constrained least squares spectral unmixing by simplex projection, " Geoscience and Remote Sensing, IEEE Transactions on 49(11), 4112-4122 (2011).
9. Dobigeon, N., Moussaoui, S., Coulon, M., Tourneret, J. Y., and Hero, A., "Joint bayesian endmember extraction and linear unmixing for hyperspectral imagery, " Signal Processing, IEEE Transactions on 57(11), 4355-4368 (2009).
10. Mittelman, R., Dobigeon, N., and Hero, A., "Hyperspectral image unmixing using a multiresolution sticky hdp, " Signal Processing, IEEE Transactions on 60(4), 1656-1671 (2012).
11. Frey, B. J. and MacKay, D. J. C., "A revolution: Belief propagation in graphs with cycles, " in [Proc. Neural Inform. Process. Syst. Conf. ], 479-485 (1997).
12. Li, S. Z., [Markov Random Field Modeling in Image Analysis], Springer, London, 3rd ed. (2009).
13. Parker, J., Schniter, P., and Cevher, V., "Bilinear generalized approximate message passing, " in [in preparation], (2013).
14. Donoho, D. L., Maleki, A., and Montanari, A., "Message passing algorithms for compressed sensing, " Proc. Nat. Acad. Sci. 106, 18914-18919 (Nov. 2009).
15. Donoho, D. L., Maleki, A., and Montanari, A., "Message passing algorithms for compressed sensing: I. Motivation and construction, " in [Proc. Inform. Theory Workshop], 1-5 (Jan. 2010).
16. Rangan, S., "Generalized approximate message passing for estimation with random linear mixing, " in [Proc. IEEE Int. Symp. Inform. Thy. ], (Aug. 2011). (See also arXiv:1010.5141).
17. Som, S. and Schniter, P., "Compressive imaging using approximate message passing and a Markov-tree prior, " IEEE Trans. Signal Process. (to appear 2012). (see also arXiv:1108.2632).
18. Som, S. and Schniter, P., "Approximate message passing for recovery of sparse signals with Markov-random- field support structure, " presented at the Internat. Conf. Mach. Learning, (Bellevue, WA) (July 2011).
19. Ziniel, J. and Schniter, P., "Dynamic compressive sensing of time-varying signals via approximate message passing, " arXiv:1205.4080 (May 2012).
20. Schniter, P., "A message-passing receiver for BICM-OFDM over unknown clustered-sparse channels, " IEEE J. Sel. Topics Signal Process. 5, 1462-1474 (Dec. 2011).
21. Vila, J. P. and Schniter, P., "Expectation-maximization Gaussian-mixture approximate message passing, " arXiv:1207.3107 (July 2012).
22. Cooper, G. F., "The computational complexity of probabilistic inference using Bayesian belief networks, " Artificial Intelligence 42, 393-405 (1990).
23. Schniter, P., "Turbo reconstruction of structured sparse signals, " in [Proc. Conf. Inform. Science & Syst. ], 1-6 (Mar. 2010).
24. Dempster, A., Laird, N. M., and Rubin, D. B., "Maximum-likelihood from incomplete data via the EM algorithm, " J. Roy. Statist. Soc. 39, 1-17 (1977).
25. Neal, R. and Hinton, G., "A view of the EM algorithm that justifies incremental, sparse, and other variants, " in [Learning in Graphical Models], Jordan, M. I., ed., 355-368, MIT Press (1999).
26. Giannandrea, A., Raqueno, N., Messinger, D. W., Faulring, J., Kerekes, J. P., van Aardt, J., Canham, K., Hagstrom, S., Ontiveros, E., Gerace, A., Kaufman, J., Vongsy, K. M., Griffith, H., and Bartlett, B. D., "The SHARE 2012 data collection campaign, " (April 2013).