H. Grassmann, On the theory of compound colors, pp.254-64, 1854.

L. Jimenez and D. Landgrebe, Supervised classification in high-dimensional space: geometrical, statistical, and asymptotical properties of multivariate data, IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews), vol.28, issue.1, pp.39-54, 1998.
DOI : 10.1109/5326.661089

G. Poldera and G. W. Van-der-heijden, Visualization of spectral images, Visualization and Optimization Techniques, p.133, 2001.
DOI : 10.1117/12.441578

J. S. Tyo, A. Konsolakis, D. I. Diersen, and R. C. Olsen, Principal-components-based display strategy for spectral imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.41, issue.3, pp.708-718, 2003.
DOI : 10.1109/TGRS.2003.808879

V. Tsagaris and V. Anastassopoulos, Multispectral image fusion for improved RGB representation based on perceptual attributes, International Journal of Remote Sensing, vol.8, issue.15, pp.3241-3254, 2005.
DOI : 10.1016/S0165-1684(00)00273-5

J. Durand and Y. Kerr, An improved decorrelation method for the efficient display of multispectral data, International conference on pattern recognition, pp.611-619, 1989.
DOI : 10.1109/TGRS.1989.35944

C. Yang, L. Lu, H. Lin, R. Guan, X. Shi et al., A Fuzzy-Statistics-Based Principal Component Analysis (FS-PCA) Method for Multispectral Image Enhancement and Display, IEEE Transactions on Geoscience and Remote Sensing, vol.46, issue.11, pp.3937-3947, 2008.
DOI : 10.1109/TGRS.2008.2001386

X. Jia and J. Richards, Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification, IEEE Trans. on Geoscience and Remote Sensing, vol.37, issue.1, pp.538-542, 1999.

V. Tsagaris, V. Anastassopoulos, and G. Lampropoulos, Fusion of hyperspectral data using segmented PCT for color representation and classification, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.10, pp.2365-2375, 2005.
DOI : 10.1109/TGRS.2005.856104

Q. Du, N. Raksuntorn, S. Cai, and R. J. Moorhead, Color Display for Hyperspectral Imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.46, issue.6, pp.1858-1866, 2008.
DOI : 10.1109/TGRS.2008.916203

Y. Zhu, P. K. Varshney, and H. Chen, Evaluation of ica based fusion of hyperspectral images for color display, " in Information Fusion, 10th International Conference on, pp.1-7, 2007.

H. Zhang, D. W. Messinger, and E. D. Montag, Perceptual display strategies of hyperspectral imagery based on PCA and ICA, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XII, 2006.
DOI : 10.1117/12.665696

N. P. Jacobson and M. R. Gupta, Design goals and solutions for display of hyperspectral images, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.11, pp.2684-2692, 2005.
DOI : 10.1109/TGRS.2005.857623

N. P. Jacobson, M. R. Gupta, and J. B. Cole, Linear Fusion of Image Sets for Display, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.10, pp.3277-3288, 2007.
DOI : 10.1109/TGRS.2007.903598

M. Cui, A. Razdan, J. Hu, and P. Wonka, Interactive hyperspectral image visualization using convex optimization, IEEE Trans. on Geoscience and Remote Sensing, vol.47, issue.6, p.1673, 2009.

S. , L. Moan, A. Mansouri, J. Y. Hardeberg, and Y. Voisin, A classseparability-based method for multi/hyperspectral image color visualization, IEEE International Conference on Image Processing, 2010.

P. Bajcsy and P. Groves, Methodology for hyperspectral band selection Photogrammetric engineering and remote sensing, pp.793-802, 2004.

C. I. Chang and S. Wang, Constrained band selection for hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.44, issue.6, pp.1575-1585, 2006.
DOI : 10.1109/TGRS.2006.864389

C. I. Chang, Q. Du, T. L. Sun, and M. L. Althouse, A joint band prioritization and band-decorrelation approach to band selection for hyperspectral image classification, IEEE Transactions on Geoscience and Remote Sensing, vol.37, issue.6, pp.2631-2641, 1999.
DOI : 10.1109/36.803411

Q. Du and H. Yang, Similarity-Based Unsupervised Band Selection for Hyperspectral Image Analysis, IEEE Geoscience and Remote Sensing Letters, vol.5, issue.4, pp.564-568, 2008.
DOI : 10.1109/LGRS.2008.2000619

B. Demir, A. Celebi, and S. Erturk, A Low-Complexity Approach for the Color Display of Hyperspectral Remote-Sensing Images Using One-Bit-Transform-Based Band Selection, IEEE Transactions on Geoscience and Remote Sensing, vol.47, issue.1, pp.97-105, 2009.
DOI : 10.1109/TGRS.2008.2001553

C. E. Shannon and W. Weaver, A Mathematical Theory of Communication, Bell System Technical Journal, vol.27, issue.3, pp.379-423, 1948.
DOI : 10.1002/j.1538-7305.1948.tb01338.x

C. Conese and F. Maselli, Selection of optimum bands from TM scenes through mutual information analysis, ISPRS Journal of Photogrammetry and Remote Sensing, vol.48, issue.3, pp.2-11, 1993.
DOI : 10.1016/0924-2716(93)90059-V

G. Qu, D. Zhang, and P. Yan, Information measure for performance of image fusion, Electronics Letters, vol.38, issue.7, p.313, 2002.
DOI : 10.1049/el:20020212

V. Tsagaris and V. Anastassopoulos, Information measure for assessing pixel-level fusion methods, Image and Signal Processing for Remote Sensing X, 2004.
DOI : 10.1117/12.565597

B. Guo, S. R. Gunn, J. Damper, and . Nelson, Band selection for hyperspectral image classification using mutual information Geoscience and Remote Sensing Letters, pp.522-526, 2006.

A. Martinez-uso, F. Pla, J. M. Sotoca, and P. Garcia-sevilla, Clustering-Based Hyperspectral Band Selection Using Information Measures, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.12, pp.4158-4171, 2007.
DOI : 10.1109/TGRS.2007.904951

C. Cariou, K. Chehdi, and S. Le-moan, Bandclust: An unsupervised band reduction method for hyperspectral remote sensing Geoscience and Remote Sensing Letters, pp.564-568, 2010.

S. Watanabe, Information Theoretical Analysis of Multivariate Correlation, IBM Journal of Research and Development, vol.4, issue.1, pp.66-82, 1960.
DOI : 10.1147/rd.41.0066

W. R. Garner, Uncertainty and structure as psychological concepts, 1962.

M. Studeny and J. Vejnarova, The Multiinformation Function as a Tool for Measuring Stochastic Dependence, Learning in graphical models, 1998.
DOI : 10.1007/978-94-011-5014-9_10

W. J. Mcgill, Multivariate information transmission, Psychometrika, vol.24, issue.2, pp.97-116, 1954.
DOI : 10.1007/BF02289159

A. J. Bell, The co-information lattice, Proceedings of the Fifth International Workshop on Independent Component Analysis and Blind Signal Separation, 2003.

S. Cai, Q. Du, and R. Moorhead, Hyperspectral imagery visualization using double layers, IEEE Trans. on Geoscience and Remote Sensing, vol.45, issue.10, pp.3028-3036, 2007.

H. Trussell, Color and multispectral image representation and display Handbook of image and video processing, p.411, 2005.

C. Simon, U. Huxhagen, A. Mansouri, A. Heritage, F. Boochs et al., Integration of high-resolution spatial and spectral data acquisition systems to provide complementary datasets for cultural heritage applications, Computer Vision and Image Analysis of Art, p.75310, 2010.
DOI : 10.1117/12.838891
URL : https://hal.archives-ouvertes.fr/hal-00638588

S. M. Nascimento, F. P. Ferreira, and D. H. Foster, Statistics of spatial cone-excitation ratios in natural scenes, Journal of the Optical Society of America A, vol.19, issue.8, pp.1484-1490, 2002.
DOI : 10.1364/JOSAA.19.001484

D. L. Ruderman, The statistics of natural images, Network: Computation in Neural Systems, vol.5, issue.4, pp.517-548, 1994.
DOI : 10.1088/0954-898X_5_4_006

N. R. Pal and S. K. , Entropy: a new definition and its applications, IEEE Transactions on Systems, Man, and Cybernetics, vol.21, issue.5, pp.1260-1270, 1991.
DOI : 10.1109/21.120079

S. Somani, B. J. Killian, and M. K. Gilson, Sampling conformations in high dimensions using low-dimensional distribution functions, The Journal of Chemical Physics, vol.130, issue.13, p.134102, 2009.
DOI : 10.1063/1.3088434