Unmixing hyperspectral images using a normal compositional model and MCMC methods

IEEE Workshop on Statistical Signal Processing Proceedings

Volumn , Issue , 2009, Pages 646-649

  Eches, O ^a   Dobigeon, N ^a,b   Mailhes, C ^a   Tourneret, J Y ^a  

^a UNIVERSITÉ DE TOULOUSE   (France)

^b The Department of Civil and Environmental Engineering   (United States)

Author keywords

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

Indexed keywords

ADDITIVITY; BAYESIAN ALGORITHMS; BAYESIAN ESTIMATORS; BAYESIAN INFERENCE; COMPOSITIONAL MODELS; CONJUGATE PRIOR; ENDMEMBER EXTRACTION ALGORITHMS; ENDMEMBERS; GAUSSIAN VECTOR; GIBBS ALGORITHMS; HYPER-PARAMETER; HYPER-SPECTRAL IMAGES; LINEAR COMBINATIONS; MCMC METHOD; MODEL UNCERTAINTIES; POSTERIOR DISTRIBUTIONS; REAL IMAGES; SIMULATION RESULT; SPECTRAL UNMIXING; UNKNOWN PARAMETERS; UNMIXING;

ALGORITHMS; BAYESIAN NETWORKS; COMPUTATIONAL COMPLEXITY; INFERENCE ENGINES; SIGNAL PROCESSING; SIMULATORS; TARGET TRACKING; UNCERTAINTY ANALYSIS;

MONTE CARLO METHODS;

EID: 72349090162     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/SSP.2009.5278494     Document Type: Conference Paper

References (11)

1. N. Keshava and J. Mustard, "Spectral unmixing," IEEE Signal Processing Magazine, pp. 44-56, Jan. 2002.
2. M. E. Winter, "Fast autonomous spectral end-member determination in hyperspectral data," in Proc. 13th Int. Conf. on Applied Geologic Remote Sensing, vol. 2, Vancouver, April 1999, 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, April 2005.
4. M. T. Eismann and D. Stein, "Stochastic mixture modeling," in Hyperspectral Data Exploitation: Theory and Applications, C.-I. Chang, Ed. Wiley, 2007, ch. 5.
5. J. Settle, "On the relationship between spectral unmixing and subspace projection," IEEE Trans. Geosci. and Remote Sensing, vol. 34, no. 4, pp. 1045-1046, July 1996.
6. D. Manolakis, C. Siracusa, and G. Shaw, "Hyperspectral subpixel target detection using the linear mixing model," IEEE Trans. Geosci. and Remote Sensing, vol. 39, no. 7, pp. 1392-1409, July 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 Processing, vol. 56, no. 7, pp. 2684-2696, July 2008.
8. D. Stein, "Application of the normal compositional model to the anala-ysis of hyperspectral imagery," in Proc. IEEE Workshop on Advances in Techniques for Analysis of Remotely Sensed Data, Oct. 2003, pp. 44-51.
9. 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: Chapman & Hall, 1996.
10. E. Punskaya, C. Andrieu, A. Doucet, and W. Fitzgerald, "Bayesian curve fitting using MCMC with applications to signal segmentation," IEEE Trans. Signal Processing, vol. 50, no. 3, pp. 747-758, March 2002.
11. 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. and Remote Sensing, vol. 39, no. 3, pp. 529-545, March 2001.