Bayesian Estimation of Linear Mixtures Using the Normal Compositional Model. Application to Hyperspectral Imagery

IEEE Transactions on Image Processing

Volumn 19, Issue 6, 2010, Pages 1403-1413

  Eches, Olivier ^a   Dobigeon, Nicolas ^a   Mailhes, Corinne ^a   Tourneret, Jean Yves ^a  

^a UNIVERSITÉ DE TOULOUSE   (France)

Author keywords

Bayesian inference; Hyperspectral images; Monte Carlo methods; Normal compositional model; Spectral unmixing

Indexed keywords

ADDITIVITY; BAYESIAN; BAYESIAN ALGORITHMS; BAYESIAN ESTIMATIONS; BAYESIAN INFERENCE; COMPOSITIONAL MODELS; CONJUGATE PRIOR; ENDMEMBER EXTRACTION ALGORITHMS; ENDMEMBERS; GAUSSIAN VECTOR; GIBBS SAMPLERS; HYPER-SPECTRAL IMAGES; HYPERSPECTRAL IMAGERY; LINEAR COMBINATIONS; LINEAR MIXTURES; MODEL UNCERTAINTIES; NOISE VARIANCE; REAL IMAGES; SPECTRAL UNMIXING; UNMIXING; VERTEX COMPONENT ANALYSIS;

ALGORITHMS; BAYESIAN NETWORKS; INFERENCE ENGINES; REMOTE SENSING; UNCERTAINTY ANALYSIS;

MONTE CARLO METHODS;

ALGORITHM; ARTICLE; AUTOMATED PATTERN RECOGNITION; BAYES THEOREM; COMPUTER ASSISTED DIAGNOSIS; COMPUTER SIMULATION; IMAGE ENHANCEMENT; METHODOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING; STATISTICAL MODEL;

ALGORITHMS; BAYES THEOREM; COMPUTER SIMULATION; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; LINEAR MODELS; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED;

EID: 77952616442     PISSN: 10577149     EISSN: None     Source Type: Journal    
DOI: 10.1109/TIP.2010.2042993     Document Type: Article

References (22)

1. N. Keshava and J. Mustard, "Spectral unmixing, " IEEE Signal Process. Mag., vol. 19, no. 1, pp. 44-56, Jan. 2002.
2. M. E. Winter, "Fast autonomous spectral endmember determination in hyperspectral data, " in Proc. 13th Int. Conf. Appl. Geologic Remote Sens., Vancouver, BC, Canada, Apr. 1999, vol. 2, pp. 337-344.
3. J. M. Nascimento and J. M. Bioucas-Dias, "Vertex component analysis: A fast algorithm to unmix hyperspectral data, " IEEE Trans. Geosci. and Remote Sensing, vol. 43, no. 4, pp. 898-910, Apr. 2005.
4. M. T. Eismann and D. Stein, "Stochastic mixture modeling, " in Hyperspectral Data Exploitation: Theory and Applications, C. Chang, Ed. New York: Wiley, 2007, ch. 5.
5. J. Settle, "On the relationship between spectral unmixing and subspace projection, " IEEE Trans. Geosci. Remote Sens., vol. 34, no. 4, pp. 1045-1046, Jul. 1996.
6. D. Manolakis, C. Siracusa, and G. Shaw, "Hyperspectral subpixel target detection using the linear mixing model, " IEEE Trans. Geosci. Remote Sens., vol. 39, no. 7, pp. 1392-1409, Jul. 2001.
7. N. Dobigeon, J.-Y. Tourneret, and C.-I. Chang, "Semi-supervised linear spectral using a hierarchical Bayesian model for hyperspectral imagery, " IEEE Trans. Signal Process., vol. 56, no. 7, pp. 2684-2696, Jul. 2008.
8. C. P. Robert and G. Casella, Monte Carlo Statistical Methods, 2nd ed. New York: Springer Verlag, 2004.
9. D. Stein, "Application of the normal compositional model to the analaysis of hyperspectral imagery, " in Proc. IEEE Workshop Adv. Tech. for Anal. of Remotely Sens. Data, Oct. 2003, pp. 44-51.
10. J. Diebolt and E. H. S. Ip, "Stochastic EM: Method and application, " in Markov Chain Monte Carlo in Practice, W. R. Gilks, S. Richardson, and D. J. Spiegelhalter, Eds. London, U. K.: Chapman & Hall, 1996.
11. S. Moussaoui, H. Hauksdóttir, F. Schmidt, C. Jutten, J. Chanussot, D. Brie, S. Douté, and J. A. Benediksson, "On the decomposition of mars hyperspectral data by ICA and Bayesian positive source separation, " Neurocomputing, vol. 71, no. 10-12, pp. 2194-2208, Jan. 2008.
12. N. Bali and A. Mohammad-Djafari, "Bayesian approach with hidden Markov modeling and mean field approximation for hyperspectral data analysis, " IEEE Trans. Image Process., vol. 17, no. 2, pp. 217-225, Feb. 2008.
13. H. Snoussi, "Efficient Bayesian spectral matching separation in noisy mixtures, " IEEE Trans. Image Process., to be published.
14. E. Punskaya, C. Andrieu, A. Doucet, and W. Fitzgerald, "Bayesian curve fitting using MCMC with applications to signal segmentation, " IEEE Trans. Signal Process., vol. 50, no. 3, pp. 747-758, Mar. 2002.
15. N. Dobigeon, J.-Y. Tourneret, and M. Davy, "Joint segmentation of piecewise constant autoregressive processes by using a hierarchical model and a Bayesian sampling approach, " IEEE Trans. Signal Process., vol. 55, no. 4, pp. 1251-1263, Apr. 2007.
16. "ENVI User's Guide Version 4.0, " RSI (Research Systems Inc), Boulder, CO, 2003.
17. R. O. Green et al., "Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS), " Remote Sens. Environ., vol. 65, no. 3, pp. 227-248, Sep. 1998.
18. D. C. Heinz and C.-I. Chang, "Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery, " IEEE Trans. Geosci. Remote Sens., vol. 39, no. 3, pp. 529-545, Mar. 2001.
19. 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. 765-776, Mar. 2007.
20. M. D. Plumbley and E. Oja, "A nonnegative-PCA algorithm for independent component analysis, " IEEE Trans. Neural Netw., vol. 15, no. 1, pp. 66-76, Jan. 2004.
21. J. W. Boardman, F. Kruse, and R. O. Green, "Mapping target signatures via partial unmixing of AVIRIS data, " in Summaries of Fifth Annual JPL Airborne Earth Science Workshop. Pasadena, CA: JPL, 1995, pp. 23-26.
22. O. Eches, N. Dobigeon, C. Mailhes, and J. Y. Tourneret, "Unmixing hyperspectral images using a normal compositional model and MCMC methods, " Univ. Toulouse, Toulouse, France, Mar. 2009 [Online]. Available: http://eches.perso.enseeiht.fr