X. Setup, F. Atkinson, S. Kamalabadi, D. Mohan, and . Jones, Tomography 6.2.1 Plan of the PIXSCAN prototype Figure 6.3: Plan of the prototype PIXSCAN Bibliography [1] I, Wavelet-based 2-d multichannel signal estimation. Image Processing, 2000.

R. Bellman, DYNAMIC PROGRAMMING AND LAGRANGE MULTIPLIERS, Proceedings of the National Academy of Sciences
DOI : 10.1073/pnas.42.10.767

URL : http://doi.org/10.1073/pnas.42.10.767

J. Benediktsson, J. Palmason, and J. Sveinsson, Classification of hyperspectral data from urban areas based on extended morphological profiles. Geoscience and Remote Sensing, IEEE Transactions on, vol.43, issue.3, pp.480-491, 2005.

J. Bioucas-dias and J. Nascimento, Hyperspectral subspace identification. Geoscience and Remote Sensing, IEEE Transactions on, vol.46, issue.8, pp.2435-2445, 2008.
DOI : 10.1109/tgrs.2008.918089

URL : http://www.lx.it.pt/~bioucas/files/hysime_ieeetgrs_08.pdf

J. D. Carroll and J. J. Chang, Analysis of individual differences in multidimensional scaling via an n-way generalization of ???Eckart-Young??? decomposition, Psychometrika, vol.12, issue.3, pp.283-319, 1970.
DOI : 10.1007/BF02310791

J. D. Carroll and J. Chang, Analysis of individual differences in multidimensional scaling via an n-way generalization of ???Eckart-Young??? decomposition, Psychometrika, vol.12, issue.3, pp.283-319, 1970.
DOI : 10.1007/BF02310791

E. Ceulemans and H. A. Kiers, Selecting among three-mode principal component models of different types and complexities: A numerical convex hull based method, British Journal of Mathematical and Statistical Psychology, vol.31, issue.1, pp.133-150, 2006.
DOI : 10.1007/978-3-642-95461-0_2

C. Chang, Hyperspectral Imaging: Techniques for Spectral Detection and Classification, Kluwer Academic, 2003.
DOI : 10.1007/978-1-4419-9170-6

C. Chang and Q. Du, Estimation of number of spectrally distinct signal sources in hyperspectral imagery. Geoscience and Remote Sensing, IEEE Transactions on, vol.42, issue.3, pp.608-619, 2004.

J. Chanussot, C. Collet, and K. Chehdi, Multivariate Image Processing. Wiley- ISTE, British Library Cataloguing-in-Publication Data, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00449464

K. Chen, T. Li, and T. Cao, Tribe-PSO: A novel global optimization algorithm and its application in molecular docking, Chemometrics and Intelligent Laboratory Systems, vol.82, issue.1-2, pp.248-259, 2006.
DOI : 10.1016/j.chemolab.2005.06.017

T. Chen, The past, present, and future of image and multidimensional signal processing, IEEE Signal Processing Magazine, vol.15, issue.2, pp.21-58, 1998.
DOI : 10.1109/79.664673

Y. Chenghai, J. H. Everitt, D. Qian, L. Bin, and J. Chanussot, Using highresolution airborne and satellite imagery to assess crop growth and yield variability for precision agriculture, Proc. IEEE, pp.582-592, 2013.

D. Cherifi, I. Hafnaoui, and A. Ali, Multimodal score-level fusion using hybrid ga-pso for multibiometric system, Informatica, vol.39, pp.209-216, 2005.
URL : https://hal.archives-ouvertes.fr/hal-01568413

R. N. Clark, G. A. Swayze, K. E. Livo, R. F. Kokaly, S. J. Sutley et al., Imaging spectroscopy: Earth and planetary remote sensing with the USGS Tetracorder and expert systems, Journal of Geophysical Research: Planets, vol.108, issue.E12, 2003.
DOI : 10.1029/2002JE001975

M. Clerc, Tribes-un exemple d'oxptimisation par essaim particulaire sans parametres de contrôle. Optimisation par Essaim Particulaire, p.64, 2003.

A. R. Conn, N. I. Gould, and P. Toint, A Globally Convergent Augmented Lagrangian Algorithm for Optimization with General Constraints and Simple Bounds, SIAM Journal on Numerical Analysis, vol.28, issue.2, pp.545-572, 1991.
DOI : 10.1137/0728030

Y. Cooren, M. Clerc, and P. Siarry, Performance evaluation of TRIBES, an adaptive particle swarm optimization algorithm, Swarm Intelligence, vol.34, issue.4, pp.149-178, 2009.
DOI : 10.1007/BFb0040810

URL : https://hal.archives-ouvertes.fr/hal-00916854

D. Muti, S. Bourennane, and J. Marot, Lower-Rank Tensor Approximation and Multiway Filtering, SIAM Journal on Matrix Analysis and Applications, vol.30, issue.3, pp.1172-1204, 2008.
DOI : 10.1137/060653263

URL : https://hal.archives-ouvertes.fr/hal-00325025

I. Daubechies, Ten lectures on wavelets, CBMS-NSF Lecture Notes, SIAM, issue.61, 1992.

L. David, Multispectral data analysis:a signal theory perspective, 1998.

L. De-lathauwer, J. Castaing, and J. Cardoso, Fourth-Order Cumulant-Based Blind Identification of Underdetermined Mixtures, IEEE Transactions on Signal Processing, vol.55, issue.6, pp.2965-2973, 2007.
DOI : 10.1109/TSP.2007.893943

L. De-lathauwer, B. De, J. Moor, and . 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

L. De-lathauwer, B. De, J. Moor, and . Vandewalle, ) Approximation of Higher-Order Tensors, SIAM Journal on Matrix Analysis and Applications, vol.21, issue.4, pp.1324-1342, 2000.
DOI : 10.1137/S0895479898346995

A. Depeursinge, A. Foncubierta-rodriguez, D. Van-de-ville, and H. Müller, Three-dimensional solid texture analysis in biomedical imaging: Review and opportunities, Medical Image Analysis, vol.18, issue.1, pp.176-196, 2014.
DOI : 10.1016/j.media.2013.10.005

URL : https://infoscience.epfl.ch/record/196101/files/depeursinge1401.pdf

Y. Dong, J. Gu, N. Li, X. Hou, and W. Yan, Combination of Genetic Algorithm and Ant Colony Algorithm for Distribution Network Planning, 2007 International Conference on Machine Learning and Cybernetics, pp.999-1002, 2007.
DOI : 10.1109/ICMLC.2007.4370288

D. Donoho, High-dimensional data analysis : the curse and blessing of dimensionality . Math Challenges of the 21st Century, 2000.

M. Dupont, Photon Counting Spectral Tomography: Development of the Prototype PIXSCAN and Proof of Concept. Theses, 2014.
URL : https://hal.archives-ouvertes.fr/tel-01019735

R. C. Eberhart and J. Kennedy, A new optimizer using particle swarm theory, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science, pp.39-43, 1995.
DOI : 10.1109/MHS.1995.494215

Y. Eckart and G. Young, The approximation of one matrix by another of lower rank, Psychometrika, vol.1, issue.3, pp.211-218, 1936.
DOI : 10.1007/BF02288367

Y. Eckart and G. Young, The approximation of one matrix by another of lower rank, Psychometrika, vol.1, issue.3, pp.211-218, 1936.
DOI : 10.1007/BF02288367

J. Ellis, H. Davis, and J. Zamudio, Exploring for onshore oil seeps with hyperspectral imaging, Oil Gas J, vol.99, issue.37, pp.49-58, 2001.

C. D. Everard, M. S. Kim, and H. Lee, A comparison of hyperspectral reflectance and fluorescence imaging techniques for detection of contaminants on spinach leaves, Journal of Food Engineering, vol.143, pp.139-145, 2014.
DOI : 10.1016/j.jfoodeng.2014.06.042

F. Gao and L. Han, Implementing the Nelder-Mead simplex algorithm with??adaptive parameters, Computational Optimization and Applications, vol.95, issue.1, pp.259-277, 2010.
DOI : 10.1016/0022-247X(83)90112-9

P. Ghosh, M. Mitchell, J. A. Tanyi, and A. Y. Hung, Incorporating priors for medical image segmentation using a genetic algorithm, Neurocomputing, vol.195, 2016.
DOI : 10.1016/j.neucom.2015.09.123

D. E. Goldberg, Genetic algorithms in search optimization and machine learning, 1989.

A. Green, M. Berman, P. Switzer, and M. Craig, A transformation for ordering multispectral data in terms of image quality with implications for noise removal. Geoscience and Remote Sensing, IEEE Transactions on, vol.26, issue.1, pp.65-74, 1988.

R. O. Green, C. M. Sarture, C. J. Chovit, J. A. Faust, P. Hajek et al., AVIRIS: A New Approach to Earth Remote Sensing, Optics and Photonics News, vol.6, issue.1, p.30, 1995.
DOI : 10.1364/OPN.6.1.000030

A. M. Grigoryan, E. R. Dougherty, and S. S. Agaian, Optimal Wiener and homomorphic filtration: Review, Signal Processing, vol.121, pp.111-138, 2016.
DOI : 10.1016/j.sigpro.2015.11.006

R. A. Harshman, Foundations of the parafac procedure: Models and conditions for an "explanatory" multi-modal factor analysis. UCLA working papers in phonetics, pp.1-84, 1970.

R. A. Harshman, Book Review : Three-Mode Principal Component Analysis, Applied Psychological Measurement, vol.31, issue.3, pp.327-332, 1985.
DOI : 10.1007/BF02289464

R. Henrion, N-way principal component analysis theory, algorithms and applications, Chemometrics and Intelligent Laboratory Systems, vol.25, issue.1, pp.1-23, 1994.
DOI : 10.1016/0169-7439(93)E0086-J

R. D. Hewson, T. J. Cudahy, M. Caccetta, A. Rodger, M. Jones et al., Advances in hyperspectral processing for province- and continental- wide mineral mapping, 2009 IEEE International Geoscience and Remote Sensing Symposium, pp.701-704, 2009.
DOI : 10.1109/IGARSS.2009.5417473

F. L. Hitchcock, The Expression of a Tensor or a Polyadic as a Sum of Products, Journal of Mathematics and Physics, vol.6, issue.1-4, pp.164-189, 1927.
DOI : 10.1002/sapm192761164

F. L. Hitchcock, Multiple Invariants and Generalized Rank of a P-Way Matrix or Tensor, Journal of Mathematics and Physics, vol.7, issue.1-4, pp.39-79, 1927.
DOI : 10.1002/sapm19287139

J. Holland, Adaptation In Natural And Artificial Systems, 1992.

J. H. Holland, Genetic Algorithms, Scientific American, vol.267, issue.1, pp.66-72, 1992.
DOI : 10.1038/scientificamerican0792-66

A. Hornberg, Handbook of Machine Vision, 2006.
DOI : 10.1002/9783527610136

H. Hudson and T. C. Lee, Maximum likelihood restoration and choice of smoothing parameter in deconvolution of image data subject to Poisson noise, Computational Statistics & Data Analysis, vol.26, issue.4, pp.393-410, 1998.
DOI : 10.1016/S0167-9473(97)00041-8

D. Jones, C. Pertunen, and B. Stuckman, Lipschitzian optimization without the Lipschitz constant, Journal of Optimization Theory and Applications, vol.20, issue.1, pp.157-181, 1993.
DOI : 10.1007/BF00941892

C. Juang, A Hybrid of Genetic Algorithm and Particle Swarm Optimization for Recurrent Network Design, IEEE Transactions on Systems, Man and Cybernetics, Part B (Cybernetics), vol.34, issue.2, pp.997-1006, 2004.
DOI : 10.1109/TSMCB.2003.818557

A. Jukic, I. Kopriva, and A. Cichocki, Noninvasive diagnosis of melanoma with tensor decomposition-based feature extraction from clinical color image, Biomedical Signal Processing and Control, vol.8, issue.6, pp.755-763, 2013.
DOI : 10.1016/j.bspc.2013.07.001

J. Kennedy and R. Eberhart, Particle swarm optimization, Proceedings of ICNN'95, International Conference on Neural Networks, pp.1942-1948, 1995.
DOI : 10.1109/ICNN.1995.488968

B. N. Khoromskij and V. Khoromskaia, Low rank Tucker-type tensor approximation to classical potentials, Central European Journal of Mathematics, vol.23, issue.3, pp.523-550, 2007.
DOI : 10.2478/s11533-007-0018-0

H. Kiers, Towards a standardized notation and terminology in multiway analysis, Journal of Chemometrics, vol.56, issue.3, pp.105-122, 2000.
DOI : 10.1007/BF02294485

H. Kiers and I. Van-mechelen, Three-way component analysis: Principles and illustrative application., Psychological Methods, vol.6, issue.1, pp.84-110, 2001.
DOI : 10.1037/1082-989X.6.1.84

T. Kolda, Orthogonal Tensor Decompositions, SIAM Journal on Matrix Analysis and Applications, vol.23, issue.1, pp.243-255, 2001.
DOI : 10.1137/S0895479800368354

T. G. Kolda, Orthogonal Tensor Decompositions, SIAM Journal on Matrix Analysis and Applications, vol.23, issue.1, pp.243-255, 2001.
DOI : 10.1137/S0895479800368354

T. G. Kolda, Orthogonal Tensor Decompositions, SIAM Journal on Matrix Analysis and Applications, vol.23, issue.1, pp.243-255, 2001.
DOI : 10.1137/S0895479800368354

T. G. Kolda and B. W. Bader, Tensor Decompositions and Applications, SIAM Review, vol.51, issue.3, pp.455-500, 2009.
DOI : 10.1137/07070111X

K. Kotwal and S. Chaudhuri, Visualization of Hyperspectral Images Using Bilateral Filtering, IEEE Transactions on Geoscience and Remote Sensing, vol.48, issue.5, pp.2308-2316, 2010.
DOI : 10.1109/TGRS.2009.2037950

P. Kroonenberg, Three-mode principal component analysis, 1983.

P. Kroonenberg, Three-mode principal component analysis, 1983.

P. Kroonenberg and J. Leeuw, Principal component analysis of three-mode data by means of alternating least squares algorithms, Psychometrika, vol.45, issue.1, pp.69-97, 1980.
DOI : 10.1007/BF02293599

P. Kroonenberg and J. D. Leeuw, Principal component analysis of three-mode data by means of alternating least squares algorithms, Psychometrika, vol.45, issue.1, pp.69-97, 1980.
DOI : 10.1007/BF02293599

J. C. Lagarias, J. A. Reeds, M. H. Wright, and P. E. Wright, Convergence Properties of the Nelder--Mead Simplex Method in Low Dimensions, SIAM Journal on Optimization, vol.9, issue.1, pp.112-147, 1998.
DOI : 10.1137/S1052623496303470

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

A. N. Langville and W. J. Stewart, A Kronecker product approximate preconditioner for SANs, Numerical Linear Algebra with Applications, vol.11, issue.89, pp.723-752, 2004.
DOI : 10.1002/nla.344

H. Lanteri, M. Roche, O. Cuevas, and C. Aime, A general method to devise maximum-likelihood signal restoration multiplicative algorithms with non-negativity constraints, Signal Processing, vol.81, issue.5, pp.945-974, 2001.
DOI : 10.1016/S0165-1684(00)00275-9

URL : https://hal.archives-ouvertes.fr/hal-00079527

L. D. Lathauwer, K. U. Leuven, and E. E. Dept, Signal processing based on Multilinear Algebra, ESAT), 1997.

L. D. Lathauwer, B. D. Moor, and J. Vandewalle, A Multilinear Singular Value Decomposition, SIAM Journal on Matrix Analysis and Applications, vol.21, issue.4, pp.1253-78, 2000.
DOI : 10.1137/S0895479896305696

L. D. Lathauwer, B. D. Moor, and J. Vandewalle, ) Approximation of Higher-Order Tensors, SIAM Journal on Matrix Analysis and Applications, vol.21, issue.4, pp.1324-1342, 2000.
DOI : 10.1137/S0895479898346995

L. D. Lathauwer, B. D. Moor, and J. Vandewalle, ) Approximation of Higher-Order Tensors, SIAM Journal on Matrix Analysis and Applications, vol.21, issue.4, pp.1324-1342, 2000.
DOI : 10.1137/S0895479898346995

D. Letexier and S. Bourennane, Adaptive Flattening for Multidimensional Image Restoration, IEEE Signal Processing Letters, vol.15, pp.229-232, 2008.
DOI : 10.1109/LSP.2007.916045

URL : https://hal.archives-ouvertes.fr/hal-00201970

D. Letexier and S. Bourennane, Noise Removal From Hyperspectral Images by Multidimensional Filtering, IEEE Transactions on Geoscience and Remote Sensing, vol.46, issue.7, pp.2061-2069, 2008.
DOI : 10.1109/TGRS.2008.916641

URL : https://hal.archives-ouvertes.fr/hal-00204097

D. Letexier, S. Bourennane, and J. Blanc-talon, Main flattening directions and Quadtree decomposition for multi-way Wiener filtering, Signal, Image and Video Processing, vol.153, issue.2, pp.253-256, 2007.
DOI : 10.1007/s11760-007-0022-7

URL : https://hal.archives-ouvertes.fr/hal-00167871

D. Letexier, S. Bourennane, and J. Blanc-talon, Nonorthogonal Tensor Matricization for Hyperspectral Image Filtering, IEEE Geoscience and Remote Sensing Letters, vol.5, issue.1, pp.3-7, 2008.
DOI : 10.1109/LGRS.2007.905117

URL : https://hal.archives-ouvertes.fr/hal-00167856

S. Lewis, A. Hudak, R. Ottmar, P. Robichaud, L. Lentile et al., Using hyperspectral imagery to estimate forest floor consumption from wildfire in boreal forests of Alaska, USA, International Journal of Wildland Fire, vol.20, issue.2, pp.255-271, 2011.
DOI : 10.1071/WF09081

L. L. De, M. B. De, and V. Joos, A multilinear singular value decomposition, SIAM Journal on Matrix Analysis and Applications, vol.21, issue.4, pp.1253-1278, 2000.

T. M. Lillesand, R. W. Kiefer, and J. W. Chipman, Remote sensing and image interpretation, 2004.

T. Lin and S. Bourennane, Hyperspectral Image Processing by Jointly Filtering Wavelet Component Tensor, IEEE Transactions on Geoscience and Remote Sensing, vol.51, issue.6, pp.3529-3541, 2013.
DOI : 10.1109/TGRS.2012.2225065

URL : https://hal.archives-ouvertes.fr/hal-01281015

T. Lin and S. Bourennane, Survey of hyperspectral image denoising methods based on tensor decompositions, EURASIP Journal on Advances in Signal Processing, vol.319, issue.12, p.2013
DOI : 10.1137/060653263

URL : https://hal.archives-ouvertes.fr/hal-01281026

K. Liu, J. P. Da-costa, H. C. So, L. Huang, and J. Ye, Detection of number of components in CANDECOMP/PARAFAC models via minimum description length, Digital Signal Processing, vol.51, pp.110-123, 2016.
DOI : 10.1016/j.dsp.2016.01.003

M. Liu, Y. Liu, H. Hu, and L. Nie, Genetic algorithm and mathematical morphology based binarization method for strip steel defect image with non-uniform illumination, Journal of Visual Communication and Image Representation, vol.37, pp.70-77, 2016.
DOI : 10.1016/j.jvcir.2015.04.005

N. Liu, B. Zhang, J. Yan, Z. Chen, W. Liu et al., Text representation: from vector to tensor, Fifth IEEE International Conference on Data Mining, pp.725-728, 2005.

X. Liu, S. Bourennane, and C. Fossati, Denoising of Hyperspectral Images Using the PARAFAC Model and Statistical Performance Analysis, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.10, pp.503717-3724, 2012.
DOI : 10.1109/TGRS.2012.2187063

URL : https://hal.archives-ouvertes.fr/hal-01280605

X. Liu, S. Bourennane, and C. Fossati, Denoising of Hyperspectral Images Using the PARAFAC Model and Statistical Performance Analysis, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.10, pp.3717-3724, 2012.
DOI : 10.1109/TGRS.2012.2187063

URL : https://hal.archives-ouvertes.fr/hal-01280605

X. Liu, S. Bourennane, and C. Fossati, Nonwhite Noise Reduction in Hyperspectral Images, IEEE Geoscience and Remote Sensing Letters, vol.9, issue.3, pp.368-372, 2012.
DOI : 10.1109/LGRS.2011.2169041

URL : https://hal.archives-ouvertes.fr/hal-01280594

X. Liu, S. Bourennane, and C. Fossati, Reduction of signal-dependent noise from hyperspectral images for target detection, IEEE trans. Geoscience and Remote Sensing, vol.52, issue.9, pp.5396-5411, 2014.

K. Man, K. S. Tang, and S. Kwong, Genetic algorithms: concepts and designs, 2012.
DOI : 10.1007/978-1-4471-0577-0

J. Marot and S. Bourennane, Particle swarm optimization for blurred contour retrieval, Signal Processing Conference (EUSIPCO), 2014 Proceedings of the 22nd European, pp.810-814, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01281145

J. Marot and S. Bourennane, Advanced Concepts for Intelligent Vision Systems, 16th International Conference Proceedings, chapter Improvement of a Wavelet-Tensor Denoising Algorithm by Automatic Rank Estimation, pp.779-790, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01280934

J. Marot, Y. Caulier, A. Kuleschov, K. Spinnler, and S. Bourennane, Advanced Concepts for Intelligent Vision Systems, 10th International Conference Proceedings, chapter Contour Detection for Industrial Image Processing by Means of Level Set Methods, pp.655-663, 2008.
URL : https://hal.archives-ouvertes.fr/hal-01280934

J. Marot, C. Fossati, and S. Bourennane, About Advances in Tensor Data Denoising Methods, EURASIP Journal on Advances in Signal Processing, vol.2008, issue.1, pp.1-13, 2008.
DOI : 10.1007/s001380050046

URL : https://hal.archives-ouvertes.fr/hal-00311886

S. Mirjalili, S. M. Mirjalili, and A. Lewis, Grey wolf optimizer Advances in Engineering Software, pp.46-61, 2014.

M. Mitchell, An introduction to genetic algorithms, 1998.

J. Mocks, Topographic components model for event-related potentials and some biophysical considerations, IEEE Transactions on Biomedical Engineering, vol.35, issue.6, pp.482-484, 1988.
DOI : 10.1109/10.2119

N. Mohananthini and G. Yamuna, Comparison of multiple watermarking techniques using genetic algorithms, Journal of Electrical Systems and Information Technology, vol.3, issue.1, 2016.
DOI : 10.1016/j.jesit.2015.11.009

P. Moradi and M. Gholampour, A hybrid particle swarm optimization for feature subset selection by integrating a novel local search strategy, Applied Soft Computing, vol.43, pp.117-130, 2016.
DOI : 10.1016/j.asoc.2016.01.044

O. Morozov, M. Unser, and P. Hunziker, Reconstruction of Large, Irregularly Sampled Multidimensional Images. A Tensor-Based Approach, IEEE Transactions on Medical Imaging, vol.30, issue.2, pp.366-374, 2011.
DOI : 10.1109/TMI.2010.2078832

D. Muti and S. Bourennane, Multidimensional filtering based on a tensor approach, Signal Processing, vol.85, issue.12, pp.2338-2353, 2005.
DOI : 10.1016/j.sigpro.2004.11.029

URL : https://hal.archives-ouvertes.fr/hal-00082677

D. Muti and S. Bourennane, Multidimensional filtering based on a tensor approach, Signal Processing, vol.85, issue.12, pp.2338-2353, 2005.
DOI : 10.1016/j.sigpro.2004.11.029

URL : https://hal.archives-ouvertes.fr/hal-00082677

R. Neelamani, H. Choi, and R. Baraniuk, ForWaRD: Fourier-Wavelet Regularized Deconvolution for Ill-Conditioned Systems, IEEE Transactions on Signal Processing, vol.52, issue.2, pp.418-433, 2004.
DOI : 10.1109/TSP.2003.821103

URL : http://cmc.rice.edu/docs/docs/Nee2002Sep1ForWaRDFou.pdf

D. S. Nicholas, B. Rasmus, and B. G. Georgios, Parallel factor analysis in sensor array processing, IEEE Transactions on Signal Processing, issue.8, p.48, 2000.

D. Nion, D. Sidiropoulos, and N. , A PARAFAC-based technique for detection and localization of multiple targets in a MIMO radar system, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing, 2009.
DOI : 10.1109/ICASSP.2009.4960024

D. Nion and N. Sidiropoulos, Tensor Algebra and Multidimensional Harmonic Retrieval in Signal Processing for MIMO Radar, IEEE Transactions on Signal Processing, vol.58, issue.11, pp.5693-5705, 2010.
DOI : 10.1109/TSP.2010.2058802

H. Othman and S. Qian, Noise reduction of hyperspectral imagery using hybrid spatial-spectral derivative-domain wavelet shrinkage. Geoscience and Remote Sensing, IEEE Transactions on, vol.44, issue.2, pp.397-408, 2006.

P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.7, pp.629-639, 1990.
DOI : 10.1109/34.56205

URL : http://www.cs.huji.ac.il/~cheny/reading/diffusion/classic.pdf

S. Prasad, W. Li, J. E. Fowler, and L. M. Bruce, Information Fusion in the Redundant-Wavelet-Transform Domain for Noise-Robust Hyperspectral Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.9, pp.1-13, 2012.
DOI : 10.1109/TGRS.2012.2185053

W. T. Reeves, Particle Systems---a Technique for Modeling a Class of Fuzzy Objects, ACM Transactions on Graphics, vol.2, issue.2, pp.91-108, 1983.
DOI : 10.1145/357318.357320

N. Renard and S. Bourennane, Improvement of Target Detection Methods by Multiway Filtering, IEEE Transactions on Geoscience and Remote Sensing, vol.46, issue.8, pp.2407-2417, 2008.
DOI : 10.1109/TGRS.2008.918419

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

N. Renard and S. Bourennane, Dimensionality Reduction Based on Tensor Modeling for Classification Methods, IEEE Transactions on Geoscience and Remote Sensing, vol.47, issue.4, pp.1123-1131, 2009.
DOI : 10.1109/TGRS.2008.2008903

N. Renard, S. Bourennane, and J. Blanc-talon, Denoising and Dimensionality Reduction Using Multilinear Tools for Hyperspectral Images, IEEE Geoscience and Remote Sensing Letters, vol.5, issue.2, pp.138-142, 2008.
DOI : 10.1109/LGRS.2008.915736

URL : https://hal.archives-ouvertes.fr/hal-00174308

N. Renard, S. Bourennane, and J. Blanc-talon, Denoising and Dimensionality Reduction Using Multilinear Tools for Hyperspectral Images, IEEE Geoscience and Remote Sensing Letters, vol.5, issue.2, pp.138-142, 2008.
DOI : 10.1109/LGRS.2008.915736

URL : https://hal.archives-ouvertes.fr/hal-00174308

N. Renard, S. Bourennane, and J. Blanc-talon, Denoising and dimensionality reduction using multilinear tools for hyperspectral images. Geoscience and Remote Sensing Letters, IEEE, vol.5, issue.2, pp.138-142, 2008.
DOI : 10.1109/lgrs.2008.915736

URL : https://hal.archives-ouvertes.fr/hal-00174308

D. Schonfeld and N. Bouaynaya, A new method for multidimensional optimization and its application in image and video processing, IEEE Signal Processing Letters, vol.13, issue.8, pp.485-488, 2006.
DOI : 10.1109/LSP.2006.873142

H. Schwefel and G. Rudolph, Contemporary Evolution Strategies. Taylor and francis Group edition, 1995.
DOI : 10.1007/3-540-59496-5_351

URL : ftp://ftp.cse.cuhk.edu.hk/pub/EC/ES/papers/ecal95.ps.gz

S. Schweizer and J. Moura, Efficient detection in hyperspectral imagery, IEEE Transactions on Image Processing, vol.10, issue.4, pp.584-597, 2001.
DOI : 10.1109/83.913593

URL : http://www.ece.cmu.edu/~moura/papers/schweitzer_effhyperspectral.pdf

G. Shaw and D. Manolakis, Signal processing for hyperspectral image exploitation, IEEE Signal Processing Magazine, vol.19, issue.1, pp.12-16, 2002.
DOI : 10.1109/79.974715

N. Sidiropoulos and R. Bro, On the uniqueness of multilinear decomposition ofN-way arrays, Journal of Chemometrics, vol.48, issue.3, pp.229-239, 2000.
DOI : 10.1002/1099-128X(200005/06)14:3<229::AID-CEM587>3.0.CO;2-N

N. Sidiropoulos, G. Giannakis, and R. Bro, Blind PARAFAC receivers for DS-CDMA systems, IEEE Transactions on Signal Processing, vol.48, issue.3, pp.810-823, 2000.
DOI : 10.1109/78.824675

URL : http://spincom.ece.umn.edu/papers/journal/2000/SP_Parafac.pdf

A. Smolic, P. Kauff, S. Knorr, A. Hornung, M. Kunter et al., Three-Dimensional Video Postproduction and Processing, Proceedings of the IEEE, pp.607-625, 2011.
DOI : 10.1109/JPROC.2010.2098350

C. M. Stellman, F. M. Olchowski, and J. V. Michalowicz, WAR HORSE (wide-area reconnaissance: hyperspectral overhead real-time surveillance experiment), Automatic Target Recognition XI
DOI : 10.1117/12.445382

J. Sun and P. Yu, Window-based Tensor Analysis on High-dimensional and Multi-aspect Streams, Sixth International Conference on Data Mining (ICDM'06), pp.1076-1080, 2006.
DOI : 10.1109/ICDM.2006.169

URL : http://www.cs.cmu.edu/~spapadim/pdf/ts_icdm06.pdf

J. Sun, H. Zeng, H. Liu, Y. Lu, and Z. Chen, CubeSVD, Proceedings of the 14th international conference on World Wide Web , WWW '05, pp.382-390, 2005.
DOI : 10.1145/1060745.1060803

B. Tekin, U. Kamilov, E. Bostan, and M. Unser, Benefits of consistency in image denoising with steerable wavelets, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.1355-1358, 2013.
DOI : 10.1109/ICASSP.2013.6637872

M. E. Timmerman and H. A. Kiers, Three-mode principal components analysis: Choosing the numbers of components and sensitivity to local optima, British Journal of Mathematical and Statistical Psychology, vol.53, issue.1, pp.1-16, 2000.
DOI : 10.1348/000711000159132

K. Tiwari, M. Arora, and D. Singh, An assessment of independent component analysis for detection of military targets from hyperspectral images, International Journal of Applied Earth Observation and Geoinformation, vol.13, issue.5
DOI : 10.1016/j.jag.2011.03.007

S. Tongchim, Coarse-grained parallel genetic algorithm for solving the timetable problem, Proceedings of 3rd Annual National Symposium on Computational Science and Engineering (ANSCSE), pp.345-353, 1999.

L. R. Tucker, Problems in Measuring Change, chapter Implications of factor analysis of three-way matrices for measurement of change, pp.122-137, 1963.

L. R. Tucker, The extension of factor analysis to three-dimensional matrices, chapter, Contributions to Mathematical Psychology, 1964.

L. R. Tucker, Some mathematical notes on three-mode factor analysis, Psychometrika, vol.64, issue.3, pp.279-311, 1966.
DOI : 10.1007/BF02289464

G. Vane, R. O. Green, T. G. Chrien, H. T. Enmark, E. G. Hansen et al., The airborne visible/infrared imaging spectrometer (AVIRIS), Remote Sensing of Environment, vol.44, issue.2-3, pp.127-143, 1993.
DOI : 10.1016/0034-4257(93)90012-M

M. Vasilescu and D. Terzopoulos, Multilinear Independent Components Analysis, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), pp.547-553, 2005.
DOI : 10.1109/CVPR.2005.240

URL : http://www.mrl.nyu.edu/~dt/papers/cvpr05/cvpr05.pdf

M. A. Vasilescu and D. Terzopoulos, Multilinear Analysis of Image Ensembles: TensorFaces, Proceedings of the 7th European Conference on Computer Vision, pp.447-460, 2002.
DOI : 10.1007/3-540-47969-4_30

T. Veracini, S. Matteoli, M. Diani, and G. Corsini, Nonparametric Framework for Detecting Spectral Anomalies in Hyperspectral Images, IEEE Geoscience and Remote Sensing Letters, vol.8, issue.4, pp.666-670, 2011.
DOI : 10.1109/LGRS.2010.2099103

Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing, vol.13, issue.4, pp.600-612, 2004.
DOI : 10.1109/TIP.2003.819861

URL : http://www.cns.nyu.edu/~zwang/files/papers/ssim.pdf

Z. Wang, A. Bovik, H. Sheikh, and E. Simoncelli, Image Quality Assessment: From Error Visibility to Structural Similarity, IEEE Transactions on Image Processing, vol.13, issue.4, pp.600-612, 2004.
DOI : 10.1109/TIP.2003.819861

URL : http://www.cns.nyu.edu/~zwang/files/papers/ssim.pdf

M. Wax and T. Kailath, Detection of signals by information theoretic criteria, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.33, issue.2
DOI : 10.1109/TASSP.1985.1164557

M. Wei and D. Shen, Minimum Rank Solutions to the Matrix Approximation Problems in the Spectral Norm, SIAM Journal on Matrix Analysis and Applications, vol.33, issue.3, pp.940-957, 2012.
DOI : 10.1137/110851134

D. Williams, Y. Zheng, P. G. Davey, F. Bao, M. Shen et al., Reconstruction of 3D surface maps from anterior segment optical coherence tomography images using graph theory and genetic algorithms, Biomedical Signal Processing and Control, vol.25, pp.91-98, 2016.
DOI : 10.1016/j.bspc.2015.11.004

Z. Xue-wu, D. Yan-qiong, L. Yan-yun, S. Ai-ye, and L. Rui-yu, A vision inspection system for the surface defects of strongly reflected metal based on multi-class SVM, Expert Systems with Applications, vol.38, issue.5, pp.5930-5939, 2011.
DOI : 10.1016/j.eswa.2010.11.030

Y. Yuzeng, L. Weiqun, W. Shouqin, and Z. Yushan, Vegetational spectral characteristics in hongtoushan mining area, liaoning province: Potential of hyperspectral remote sensing in environment monitoring, Remote Sensing, pp.1-4, 2012.

Y. Zhang and P. Zhang, Machine training and parameter settings with social emotional optimization algorithm for support vector machine, Pattern Recognition Letters, vol.54, pp.36-42, 2015.
DOI : 10.1016/j.patrec.2014.11.011

Y. Zheng and Y. Liao, Parameter identification of nonlinear dynamic systems using an improved particle swarm optimization, Optik - International Journal for Light and Electron Optics, vol.127, issue.19, pp.7865-7874, 2016.
DOI : 10.1016/j.ijleo.2016.05.145

M. Zhu and S. Salcudean, Real-time image-based b-mode ultrasound image simulation of needles using tensor-product interpolation, IEEE Transactions on Medical Imaging, vol.30, issue.7, pp.1391-1400, 2011.

A. Zidi, J. Juan, J. Marot, and S. Bourennane, Nonnegative matrix factorization with spatial prior and reference spectra application to remote hyperspectral image understanding, 2014 5th European Workshop on Visual Information Processing (EUVIP), pp.1-6, 2014.
DOI : 10.1109/EUVIP.2014.7018397

URL : https://hal.archives-ouvertes.fr/hal-01281155

A. Zidi, J. Marot, S. Bourennane, and K. Spinnler, Automatic rank estimation of Parafac decomposition and application to multispectral image wavelet denoising, 2016 IEEE International Conference on Image Processing (ICIP), pp.3101-3105, 2016.
DOI : 10.1109/ICIP.2016.7532930

A. Zidi, J. Marot, S. Bourennane, and K. Spinnler, Bio-Inspired Optimization Algorithms for Automatic Estimation of Multiple Subspace Dimensions in a Tensor-Wavelet Denoising Algorithm, Journal of Remote Sensing Technology, vol.4, issue.1, pp.90-114, 2016.
DOI : 10.18005/JRST0401008

A. Zidi, J. Marot, K. Spinnler, and S. Bourennane, Unmixing of Hyperspectral Images with Pure Prior Spectral Pixels, Proceedings of the 10th International Conference on Computer Vision Theory and Applications, pp.153-158, 2015.
DOI : 10.5220/0005311101530158

URL : https://hal.archives-ouvertes.fr/hal-01281168

A. Zidi, K. Spinnler, J. Marot, and S. Bourennane, Multispectral image denoising in wavelet domain with unsupervised tensor subspace-based method, 2016 6th European Workshop on Visual Information Processing (EUVIP), pp.1-6, 2016.
DOI : 10.1109/EUVIP.2016.7764599