D. A. Landgrebe, Hyperspectral image data analysis, IEEE Signal Processing Magazine, vol.19, issue.1, pp.17-28, 2002.
DOI : 10.1109/79.974718

L. O. Jiménez, E. Arzuaga, and M. Vélez, Unsupervised Linear Feature-Extraction Methods and Their Effects in the Classification of High-Dimensional Data, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.2, pp.469-483, 2007.
DOI : 10.1109/TGRS.2006.885412

N. Renard and S. Bourennane, Improvement of Target Detection Methods by Multiway Filtering, IEEE Transactions on Geoscience and Remote Sensing, vol.46, issue.8, 2008.
DOI : 10.1109/TGRS.2008.918419

URL : https://hal.archives-ouvertes.fr/hal-00250104

M. Pesaresi and J. A. Benediktsson, A new approach for the morphological segmentation of high-resolution satellite imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.39, issue.2, pp.309-320, 2001.
DOI : 10.1109/36.905239

J. A. Palmason, J. A. Benediktsson, J. R. Sviensson, and J. Chanussot, Classification of hypersepctral data from urban areas using morphological preprocessing and ICA, in proc, IGARSS, pp.176-179, 2005.

M. Fauvel, J. A. Benediktsson, J. Chanussot, and J. Sveinsson, Spectral and Spatial Classification of Hypersepctral Data Using SVMs and Morphological Profiles, IEEE Trans. Geosci. Remote Sens, issue.11, p.46, 2008.

L. Lathauwer, B. Moor, and J. Vandewalle, A Multilinear Singular Value Decomposition, SIAM Journal on Matrix Analysis and Applications, vol.21, issue.4, pp.1253-1278, 2000.
DOI : 10.1137/S0895479896305696

G. M. Foody and A. Mathur, A relative evaluation of multiclass image classification by support vector machines, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.6, pp.1335-1343, 2004.
DOI : 10.1109/TGRS.2004.827257

F. Meyer and P. Maragos, Nonlinear Scale-Space Representation with Morphological Levelings, Journal of Visual Communication and Image Representation, vol.11, issue.2, p.245265, 2000.
DOI : 10.1006/jvci.1999.0447

G. Camps-valls, T. Bandos, and D. Zhou, Semi-Supervised Graph-Based Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.10, pp.3044-3054, 2007.
DOI : 10.1109/TGRS.2007.895416

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.94.6323

H. Huang, C. Ding, D. Luo, and T. Li, Simultaneous tensor subspace selection and clustering, Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 08, pp.327-335, 2008.
DOI : 10.1145/1401890.1401933

D. Luo, H. Huang, and C. Ding, Are Tensor Decomposition Solutions Unique? On the Global Convergence HOSVD and ParaFac Algorithms, 2009.
DOI : 10.1007/978-3-642-20841-6_13

S. Velasco-forero and J. Angulo, Morphological scale-space for hypersepctral images and dimensionality exploration using tensor modeling, First IEEE Workshop on Hyperspectral Image and Signal Processing: Emerging Remote Sensing, p.8, 2009.

A. B. Kiely and M. A. Klimesh, Exploiting Calibration-Induced Artifacts in Lossless Compression of Hyperspectral Imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.47, issue.8, pp.2672-2678, 2009.
DOI : 10.1109/TGRS.2009.2015291

L. Eldén and B. Savas, A Newton???Grassmann Method for Computing the Best Multilinear Rank-$(r_1,$ $r_2,$ $r_3)$ Approximation of a Tensor, SIAM Journal on Matrix Analysis and Applications, vol.31, issue.2, pp.248-271, 2009.
DOI : 10.1137/070688316