C. Chang, Hyperspectral Data Exploitation, 2007.
DOI : 10.1002/0470124628

N. Keshava and J. Mustard, Spectral unmixing, Spectral unmixing, pp.44-57, 2002.
DOI : 10.1109/79.974727

N. Keshava, « A survey of spectral unmixing », Lincoln Lab, J, vol.14, issue.1, pp.55-78, 2003.

N. Dobigeon, S. Moussaoui, M. Coulon, J. Tourneret, and A. O. , SAlgorithmes bay??siens pour le d??m??lange supervis??, semi-supervis?? et non-supervis?? d???images hyperspectrales, Traitement du signal, vol.27, issue.1, pp.79-108, 2010.
DOI : 10.3166/ts.27.79-108

M. Parente and A. Plaza, Survey of geometric and statistical unmixing algorithms for hyperspectral images, 2010 2nd Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pp.1-4, 2010.
DOI : 10.1109/WHISPERS.2010.5594929

C. Lawson and R. Hanson, Solving Least-Squares Problems, 1974.
DOI : 10.1137/1.9781611971217

J. J. Settle and N. A. Drake, Linear mixing and the estimation of ground cover proportions, International Journal of Remote Sensing, vol.27, issue.6, pp.1159-1177, 1993.
DOI : 10.1109/36.103288

D. Heinz and C. Chang, Fully constrained least squares linear spectral mixture analysis method for material quantification in hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.39, issue.3, pp.529-545, 2001.
DOI : 10.1109/36.911111

P. Armand, J. C. Gilbert, and S. Jan-jégou, A Feasible BFGS Interior Point Algorithm for Solving Convex Minimization Problems, SIAM Journal on Optimization, vol.11, issue.1, pp.199-222, 2000.
DOI : 10.1137/S1052623498344720
URL : https://hal.archives-ouvertes.fr/inria-00073185

M. E. Winter and «. , an algorithm for fast autonomous spectral endmember determination in hyperspectral data », in proc. of SPIE -Image Spectrometry V, pp.266-277, 1999.