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=

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