D. Stein, S. Beaven, L. Hoff, E. Winter, A. Schaum et al., Anomaly detection from hyperspectral imagery, IEEE Signal Processing Magazine, vol.19, issue.1, pp.58-69, 2002.
DOI : 10.1109/79.974730

J. M. Bioucas-dias, A. Plaza, G. Camps-valls, P. Scheunders, N. Nasrabadi et al., Hyperspectral remote sensing data analysis and future challenges Geoscience and Remote Sensing Magazine, IEEE, vol.1, issue.2, pp.6-36, 2013.

M. T. Eismann, Hyperspectral remote sensing, 2012.
DOI : 10.1117/3.899758

X. Ceamanos, S. Douté, B. Luo, F. Schmidt, G. Jouannic et al., Intercomparison and Validation of Techniques for Spectral Unmixing of Hyperspectral Images: A Planetary Case Study, IEEE Transactions on Geoscience and Remote Sensing, vol.49, issue.11, pp.4341-4358, 2011.
DOI : 10.1109/TGRS.2011.2140377

URL : https://hal.archives-ouvertes.fr/hal-00696014

J. B. Adams, INTERPRETATION OF VISIBLE AND NEAR-INFRARED DIFFUSE REFLECTANCE SPECTRA OF PYROXENES AND OTHER ROCK-FORMING MINERALS, Infrared and Raman spectroscopy of lunar and terrestrial materials, pp.91-116, 1975.
DOI : 10.1016/B978-0-12-399950-4.50009-4

J. Bibring, M. Combes, Y. Langevin, A. Soufflot, C. Cara et al., Results from the ISM experiment, Nature, vol.341, issue.6243, pp.591-593, 1989.
DOI : 10.1038/341591a0

J. Chen, C. Richard, and P. Honeine, Nonlinear Unmixing of Hyperspectral Data Based on a Linear-Mixture/Nonlinear-Fluctuation Model, IEEE Transactions on Signal Processing, vol.61, issue.2, pp.480-492, 2013.
DOI : 10.1109/TSP.2012.2222390

H. Clenet, P. C. Pinet, Y. Daydou, F. Heuripeau, C. Rosemberg et al., A new systematic approach using the Modified Gaussian Model: Insight for the characterization of chemical composition of olivines, pyroxenes and olivine???pyroxene mixtures, Icarus, vol.213, issue.1, pp.404-422, 2011.
DOI : 10.1016/j.icarus.2011.03.002

URL : https://hal.archives-ouvertes.fr/hal-00743839

P. Boccacci, Introduction to Inverse Problems in Imaging
DOI : 10.1201/9781439822067

J. Bioucas-dias and A. Plaza, An overview on hyperspectral unmixing: Geometrical, statistical, and sparse regression based approaches, Geoscience and Remote Sensing Symposium (IGARSS), pp.1135-1138, 2011.

J. Bioucas-dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du et al., Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches, Selected Topics in Applied Earth Observations and Remote Sensing, pp.354-379, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00760787

N. Dobigeon, J. Tourneret, C. Richard, J. Bermudez, S. Mclaughlin et al., Nonlinear Unmixing of Hyperspectral Images: Models and Algorithms, IEEE Signal Processing Magazine, vol.31, issue.1, pp.82-94, 2014.
DOI : 10.1109/MSP.2013.2279274

URL : https://hal.archives-ouvertes.fr/hal-00915663

B. Hapke, Theory of reflectance and emittance spectroscopy, 2012.

J. M. Nascimento and J. M. Bioucas-dias, Nonlinear mixture model for hyperspectral unmixing, Image and Signal Processing for Remote Sensing XV, pp.74-770
DOI : 10.1117/12.830492

W. Fan, B. Hu, J. Miller, and M. Li, Comparative study between a new nonlinear model and common linear model for analysing laboratory simulated???forest hyperspectral data, International Journal of Remote Sensing, vol.30, issue.11, pp.2951-2962, 2009.
DOI : 10.1029/91JE03117

A. Halimi, Y. Altmann, N. Dobigeon, and J. Tourneret, Nonlinear Unmixing of Hyperspectral Images Using a Generalized Bilinear Model, IEEE Transactions on Geoscience and Remote Sensing, vol.49, issue.11, pp.4153-4162, 2011.
DOI : 10.1109/TGRS.2010.2098414

I. Meganem, P. Deliot, X. Briottet, Y. Deville, and S. Hosseini, Linear–Quadratic Mixing Model for Reflectances in Urban Environments, IEEE Transactions on Geoscience and Remote Sensing, vol.52, issue.1, pp.544-558, 2014.
DOI : 10.1109/TGRS.2013.2242475

J. Plaza, A. J. Plaza, P. Martinez, and R. M. Perez, Nonlinear mixture models for analyzing laboratory simulated-forest hyperspectral data, Image and Signal Processing for Remote Sensing IX, pp.480-487, 2004.
DOI : 10.1117/12.511127

C. Chang and A. Plaza, A fast iterative algorithm for implementation of pixel purity index Geoscience and Remote Sensing Letters, pp.63-67, 2006.

M. E. Winter, N-findr: an algorithm for fast autonomous spectral endmember determination in hyperspectral data, pp.266-275

J. Nascimento and J. Dias, Vertex component analysis: a fast algorithm to unmix hyperspectral data Geoscience and Remote Sensing, IEEE Transactions on, vol.43, issue.4, pp.898-910, 2005.

M. Craig, Minimum-volume transforms for remotely sensed data Geoscience and Remote Sensing, IEEE Transactions on, vol.32, issue.3, pp.542-552, 1994.

M. Hollósi and G. D. Fasman, Convex constraint analysis: a natural deconvolution of circular dichroism curves of proteins, Protein Engineering, vol.4, issue.6, pp.669-679, 1991.

S. Jia and Y. Qian, Constrained nonnegative matrix factorization for hyperspectral unmixing Geoscience and Remote Sensing, IEEE Transactions on, vol.47, issue.1, pp.161-173, 2009.

L. Miao and H. Qi, Endmember extraction from highly mixed data using minimum volume constrained nonnegative matrix factorization Geoscience and Remote Sensing, IEEE Transactions on, vol.45, issue.3, pp.765-777, 2007.

P. Sajda, S. Du, and L. Parra, Recovery of constituent spectra using non-negative matrix factorization, Wavelets: Applications in Signal and Image Processing X, pp.321-331, 2003.
DOI : 10.1117/12.504676

N. Yokoya, J. Chanussot, and A. Iwasaki, Generalized bilinear model based nonlinear unmixing using semi-nonnegative matrix factorization, 2012 IEEE International Geoscience and Remote Sensing Symposium, pp.1365-1368, 2012.
DOI : 10.1109/IGARSS.2012.6351282

URL : https://hal.archives-ouvertes.fr/hal-00799660

R. Heylen and P. Scheunders, Calculation of Geodesic Distances in Nonlinear Mixing Models: Application to the Generalized Bilinear Model, Geoscience and Remote Sensing Letters, pp.644-648, 2012.
DOI : 10.1109/LGRS.2011.2177241

N. Courty, X. Gong, J. Vandel, and T. Burger, SAGA: sparse and geometry-aware non-negative matrix factorization through non-linear local embedding, Machine Learning, pp.205-226, 2014.
DOI : 10.1007/s10994-014-5463-y

URL : https://hal.archives-ouvertes.fr/hal-01018683

J. Broadwater, A. Banerjee, and P. Burlina, Kernel methods for unmixing hyperspectral imagery Kernel Methods for Remote Sensing Data Analysis, pp.249-270, 2009.

J. Broadwater and A. Banerjee, A comparison of kernel functions for intimate mixture models, 2009 First Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing, pp.1-4, 2009.
DOI : 10.1109/WHISPERS.2009.5289073

J. Broadwater, R. Chellappa, A. Banerjee, and P. Burlina, Kernel fully constrained least squares abundance estimates, 2007 IEEE International Geoscience and Remote Sensing Symposium, pp.4041-4044, 2007.
DOI : 10.1109/IGARSS.2007.4423736

J. Bieniarz, E. Aguilera, X. Zhu, R. Muller, and P. Reinartz, Joint sparsity model for multilook hyperspectral image unmixing Geoscience and Remote Sensing Letters, IEEE, vol.12, issue.4, pp.696-700, 2015.

J. Bieniarz, R. Muller, X. Zhu, and P. Reinartz, On the use of overcomplete dictionaries for spectral unmixing, 2012 4th Workshop on Hyperspectral Image and Signal Processing (WHISPERS), pp.1-4, 2012.
DOI : 10.1109/WHISPERS.2012.6874232

J. Li and J. M. Bioucas-dias, Minimum Volume Simplex Analysis: A Fast Algorithm to Unmix Hyperspectral Data, IGARSS 2008, 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008.
DOI : 10.1109/IGARSS.2008.4779330

T. Chan, W. Ma, A. Ambikapathi, and C. Chi, A simplex volume maximization framework for hyperspectral endmember extraction Geoscience and Remote Sensing, IEEE Transactions on, vol.49, issue.11, pp.4177-4193, 2011.

S. Matteoli, M. Diani, and G. Corsini, A tutorial overview of anomaly detection in hyperspectral images Aerospace and Electronic Systems Magazine, pp.5-28, 2010.

T. Veracini, S. Matteoli, M. Diani, and G. Corsini, Fully Unsupervised Learning of Gaussian Mixtures for Anomaly Detection in Hyperspectral Imagery, 2009 Ninth International Conference on Intelligent Systems Design and Applications, pp.596-601, 2009.
DOI : 10.1109/ISDA.2009.220

S. Schweizer and J. Moura, Hyperspectral imagery: clutter adaptation in anomaly detection Information Theory, IEEE Transactions on, vol.46, issue.5, pp.1855-1871, 2000.

M. Z. Baghbidi, K. Jamshidi, A. R. Nilchi, and S. Homayouni, Improvement of anomoly detection algorithms in hyperspectral images using discrete wavelet transform, 2012.

L. Chapel and C. Friguet, Anomaly Detection with Score Functions Based on the Reconstruction Error of the Kernel PCA, Lecture Notes in Computer Science, vol.8724, pp.227-241, 2014.
DOI : 10.1007/978-3-662-44848-9_15

URL : https://hal.archives-ouvertes.fr/hal-01061866

H. Hoffmann, Kernel PCA for novelty detection, Pattern Recognition, vol.40, issue.3, pp.863-874, 2007.
DOI : 10.1016/j.patcog.2006.07.009

Y. Altmann, S. Mclaughlin, and A. Hero, Robust Linear Spectral Unmixing Using Anomaly Detection, IEEE Transactions on Computational Imaging, vol.1, issue.2, pp.74-85, 2015.
DOI : 10.1109/TCI.2015.2455411

C. Thurau, K. Kersting, M. Wahabzada, and C. Bauckhage, Descriptive matrix factorization for sustainability Adopting the principle of opposites, Data Mining and Knowledge Discovery, vol.15, issue.8, pp.325-354, 2012.
DOI : 10.1007/s10618-011-0216-z

G. Camps-valls and L. Bruzzone, Kernel methods for remote sensing data analysis, 2009.
DOI : 10.1002/9780470748992

A. T. Kyrillidis, S. Becker, and V. Cevher, Sparse projections onto the simplex

R. Heylen, D. Burazerovic, and P. Scheunders, Non-Linear Spectral Unmixing by Geodesic Simplex Volume Maximization, Selected Topics in Signal Processing, pp.534-542, 2011.
DOI : 10.1109/JSTSP.2010.2088377

J. Cohen, A Coefficient of Agreement for Nominal Scales, Educational and Psychological Measurement, vol.20, issue.1, pp.37-46, 1960.
DOI : 10.1177/001316446002000104

F. Schmidt, A. Schmidt, E. Tréguier, M. Guiheneuf, S. Moussaoui et al., Implementation Strategies for Hyperspectral Unmixing Using Bayesian Source Separation, IEEE Transactions on Geoscience and Remote Sensing, vol.48, issue.11, pp.4003-4013, 2010.
DOI : 10.1109/TGRS.2010.2062190

URL : https://hal.archives-ouvertes.fr/hal-00466419

C. T. Russell, C. A. Raymond, A. Coradini, H. Y. Mcsween, M. T. Zuber et al., Dawn at Vesta: Testing the Protoplanetary Paradigm, Science, vol.336, issue.6082, pp.684-686
DOI : 10.1126/science.1219381

C. T. Russell and C. A. Raymond, The Dawn Mission to Vesta and Ceres, Space Science Reviews, issue.1-4, pp.3-23

H. Y. Mcsween, R. P. Binzel, M. C. De-sanctis, E. Ammannito, T. H. Prettyman et al., Dawn; the Vesta-HED connection; and the geologic context for eucrites, diogenites, and howardites, Meteoritics & Planetary Science, vol.31, issue.11, pp.2090-2104
DOI : 10.1111/maps.12108

E. Ammannito, M. C. De-sanctis, E. Palomba, A. Longobardo, D. W. Mittlefehldt et al., Olivine in an unexpected location on Vestas surface, Nature, pp.122-125

A. W. Beck and H. Y. Mcsween, Diogenites as polymict breccias composed of orthopyroxenite and harzburgite, Meteoritics & Planetary Science, vol.32, issue.1, pp.850-872
DOI : 10.1111/j.1945-5100.2010.01061.x

J. Combe, T. B. Mccord, L. A. Mcfadden, S. Ieva, F. Tosi et al., Composition of the northern regions of Vesta analyzed by the Dawn mission, Icarus, vol.259, pp.53-71, 2015.
DOI : 10.1016/j.icarus.2015.04.026

H. Clenet, M. Jutzi, J. Barrat, E. I. Asphaug, W. Benz et al., A deep crust???mantle boundary in the asteroid 4??Vesta, Nature, vol.216, issue.7509, pp.303-306
DOI : 10.1038/nature13499

URL : https://hal.archives-ouvertes.fr/insu-01056295

M. C. De-sanctis, A. Coradini, E. Ammannito, G. Filacchione, M. T. Capria et al., The VIR Spectrometer, Space Science Reviews, issue.14, pp.329-369

J. Anderson, K. J. Becker, T. N. Titus, M. C. De-sanctis, A. Nathues et al., Isis cartographic tools for the Dawn Framing Camera and Visual and Infrared Spectrometer, AGU Fall Meeting, pp.31-40, 2011.

B. W. Buratti, D. T. Denevi, U. Blewett, M. J. Christensen, P. Gaffey et al., Color and albedo heterogeneity of Vesta from Dawn, Science, vol.336, issue.6082, pp.700-704, 2012.

L. A. Li, D. W. Mcfadden, C. M. Mittlefehldt, R. Pieters, K. Jaumann et al., Vesta's mineralogical composition as revealed by the visible and infrared spectrometer on Dawn, Meteoritics & Planetary Science, issue.11, pp.2166-2184

J. Li, B. J. Buratti, F. Cappaccioni, M. T. Capria, L. Le-corre et al., Abstract, Asteroids, Comets, Meteors, p.6387, 2012.
DOI : 10.1017/S174392131400533X

URL : https://hal.archives-ouvertes.fr/hal-01256309

O. Ruesch, H. Hiesinger, M. C. De-sanctis, E. Ammannito, E. Palomba et al., Detections and geologic context of local enrichments in olivine on Vesta with VIR/Dawn data, Journal of Geophysical Research: Planets, vol.48, issue.11, pp.1-31, 2014.
DOI : 10.1016/j.icarus.2014.04.037