On the decomposition of Mars hyperspectral data by ICA and Bayesian positive source separation

Neurocomputing

Volumn 71, Issue 10-12, 2008, Pages 2194-2208

  Moussaoui, S ^a   Hauksdottir H ^b,c   Schmidt, F ^d   Jutten, C ^c   Chanussot, J ^c   Brie, D ^e   Doute S ^d   Benediktsson, J A ^b  

^a INSTITUT DE RECHERCHE EN COMMUNICATIONS ET CYBERNÉTIQUE DE NANTES   (France)

^b UNIVERSITY OF ICELAND   (Iceland)

^c GRENOBLE INSTITUTE OF TECHNOLOGY   (France)

^d LABORATOIRE D ASTROPHYSIQUE   (France)

^e CENTRE DE RECHERCHE EN AUTOMATIQUE DE NANCY   (France)

Author keywords

Bayesian source separation; Hyperspectral data; Independent component analysis; Mars Express mission; Positivity constraint; Source separation

Indexed keywords

BLIND SOURCE SEPARATION; FEATURE EXTRACTION; IMAGE CLASSIFICATION; IMAGE RESOLUTION; INDEPENDENT COMPONENT ANALYSIS; PIXELS;

BAYESIAN SOURCE SEPARATION; HYPERSPECTRAL DATA;

MARTIAN SURFACE ANALYSIS;

CARBON DIOXIDE; WATER;

ARTICLE; ARTIFACT; ASTRONOMY; BAYESIAN LEARNING; CLASSIFICATION ALGORITHM; DATA ANALYSIS; DATA EXTRACTION; IMAGE PROCESSING; INDEPENDENT COMPONENT ANALYSIS; INTERMETHOD COMPARISON; MATHEMATICAL MODEL; PRIORITY JOURNAL; RELIABILITY;

EID: 44649130165     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2007.07.034     Document Type: Article

References (38)

1. S.A. Abdallah, M.D. Plumbley, Polyphonic transcription by non-negative sparse coding of power spectra, in: Proceedings of the International Conference on Music Information Retrieval (ISMIR'2004), Barcelona, Spain, 2004, pp. 318-325.
2. J.-P. Bibring, et al., Mars Express: the Scientific Payload, OMEGA: Observatoire pour la Minéralogie, l'Eau, les Glaces et l'Activité, ESA SP-1240, 2004, pp. 37-49.
3. Bibring J.-P., et al. Perennial water ice identified in the south polar cap of Mars. Nature 428 (2004) 627-630
4. Bro R., and De Jong S. A fast non-negativity constrained least squares algorithm. J. Chemometrics 11 (1997) 393-401
5. Cardoso J.-F., and Souloumiac A. Blind beamforming for non Gaussian signals. IEE Proc.-F 140 6 (1993) 362-370
6. Cardoso J.-F. Blind signal separation: statistical principles. Proc. IEEE 9 10 (1998) 2009-2025
7. Chen M.-H., Shao Q.-M., and Ibrahim J.G. Monte Carlo Methods in Bayesian Computation (2000), Springer, New York
8. Cichocki A., and Amari S.-I. Adaptive Blind Signal and Image Processing-Learning Algorithms and Applications (2002), Wiley, New York
9. Comon P. Independent component analysis, a new concept?. Signal Process. 36 3 (1994) 287-314
10. Comon P., Jutten C., and Hérault J. Blind separation of sources, part II: problems statement. Signal Processs. 24 (1991) 11-20
11. D. Donoho, V. Stodden, When does non-negative matrix factorization give a correct decomposition into parts? in: Advances in Neural Information Processing Systems (NIPS'2003), Cambridge, USA, 2003.
12. Douté S., Schmitt B., Langevin Y., Bibring J.-P., Altieri F., Bellucci G., Gondet B., and Poulet F. the MEX OMEGA team, South Pole of Mars: nature and composition of the icy terrains from Mars Express OMEGA observations. Planet. Space Sci. 55 (2007) 113-133
13. Geman S., and Geman D. Stochastic relaxation, Gibbs distributions and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6 6 (1984) 721-741
14. Hastings W.K. Monte Carlo sampling methods using Markov chains and their applications. Biometrika 57 1 (1970) 97-109
15. P.O. Hoyer, Non-negative sparse coding, in: Proceedings of IEEE Workshop on Neural Networks for Signal Processing (NNSP'2002), 2002, pp. 557-565.
16. Hsiao I.-T., Rangarajan A., and Gindi G. Bayesian image reconstruction for transmission tomography using mixture model priors and deterministic annealing algorithms. J. Electron. Imaging 12 1 (2003) 7-16
17. Hyvärinen A., Karhunen J., and Oja E. Independent component analysis, Adaptive and Learning Systems for Signal Processing, Communications and Control (2001), Wiley, New York
18. Jutten C., and Hérault J. Blind separation of sources, part I: an adaptive algorithm based on neuromimetic architecture. Signal Process. 24 (1991) 1-10
19. Keshava N., and Mustard J.F. Spectral unmixing. IEEE Signal Process. Mag. 19 1 (2002) 14-57
20. Lawson C., and Hanson R. Solving Least-Squares Problems (1974), Prentice-Hall, Englewood Cliffs NJ
21. Lee D., and Seung H. Learning the parts of objects by non-negative matrix factorization. Nature 401 (1999) 788-791
22. Lin Y., and Lee D. Bayesian regularization and nonnegative deconvolution for room impulse response estimation. IEEE Trans. Signal Process. 11 3 (2006) 839-847
23. Metropolis N., Rosenbluth A., Rosenbluth M., Taller A., and Taller E. Equation of state calculation by fast computing machines. J. Chem. Phys. 21 (1953) 1087-1092
24. Miskin J., and MacKay D. Ensemble learning for blind source separation. In: Roberts S., and Everson R. (Eds). Independent Component Analysis: Principles and Practice (2001), Cambridge University Press, Cambridge, MA 209-233
25. S. Moussaoui, D. Brie, C. Carteret, A. Mohammad Djafari, Application of Bayesian non-negative source separation to mixture analysis in spectroscopy, in: R. Fischer, R. Preuss, U. von Toussaint (Eds.), MaxEnt, AIP Conference Proceedings, vol. 735, Garching, Germany, 2004, pp. 237-244.
26. S. Moussaoui, D. Brie, J. Idier, Non-negative source separation: range of admissible solutions and conditions for the uniqueness of the solution, in: Proceedings of IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'2005), Philadelphia, USA, 2005, pp. 289-292.
27. Moussaoui S., Brie D., Mohammad-Djafari A., and Carteret C. Separation of non-negative mixture of non-negative sources using a Bayesian approach and MCMC sampling. IEEE Trans. Signal Process. 54 11 (2006) 4133-4145
28. Moussaoui S., Carteret C., Brie D., and Mohammad-Djafari A. Bayesian analysis of spectral mixture data using Markov chain Monte Carlo methods. Chemometrics Intell. Lab. Syst. 81 2 (2006) 137-148
29. A. Mohammad-Djafari, A Bayesian approach to source separation, in: J.T. Rychert, G.J. Erickson, C.R. Smith (Eds.), MaxEnt, AIP Conference Proceedings, vol. 567, Boise, Idao, 1999, pp. 221-244.
30. Ochs M.F., Stoyanova R.S., Arias-Mendoza F., and Brown T.R. A new method for spectral decomposition using a bilinear Bayesian approach. J. Magn. Resona. 137 (1999) 161-176
31. Paatero P., and Tapper U. Positive matrix factorization: a nonnegative factor model with optimal utilization of error estimates of data values. Environmetrics 5 (1994) 111-126
32. Robert C. Monte Carlo Statistical Methods (1999), Springer, Berlin
33. Roberts S., and Choudrey R. Data decomposition using independent component analysis with prior constraints. Pattern Recognition 36 (2003) 1813-1825
34. Rowe D. Multivariate Bayesian Statistics: Models for Source Separation and Signal Unmixing (2003), CRC Press, Boca Raton, FL, USA
35. Schmidt F., Douté S., and Schmitt B. Wavanglet: an efficient supervised classifier for hyperspectral images. IEEE Trans. Geosci. Remote Sensing 45 5 (2007) 1374-1385
36. Snoussi H., and Idier J. Bayesian blind separation of generalized hyperbolic processes in noisy and underdeterminate mixtures processes in noisy and underdeterminate mixtures. IEEE Trans. Signal Process. 54 9 (2006) 3257-3269
37. Tanre D., Herman M., Deschamps P.Y., and de Leffe A. Atmospheric modeling for space measurements of ground reflectances, including bidirectional properties. App. Opt. 18 (1979) 3587-3594
38. Tauler R., Kowalski B., and Fleming S. Multivariate curve resolution applied to spectral data from multiple runs of an industrial process. Anal. Chem. 65 (1993) 2040-2047