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

D. Manolakis, D. Marden, and G. Shaw, Hyperspectral image processing for automatic target detection applications, Lincoln Laboratory Journal, vol.14, issue.1, pp.79-116, 2003.

D. G. Manolakis, R. B. Lockwood, and T. W. Cooley, Hyperspectral Imaging Remote Sensing: Physics, Sensors, and Algorithms, 2016.
DOI : 10.1017/CBO9781316017876

N. K. Patel, C. Patnaik, S. Dutta, A. M. Shekh, and A. J. Dave, Study of crop growth parameters using Airborne Imaging Spectrometer data, International Journal of Remote Sensing, vol.22, issue.12, pp.2401-2411, 2001.
DOI : 10.1080/01431160117383

B. Datt, T. R. Mcvicar, T. G. Van-niel, D. L. Jupp, and J. S. Pearlman, Preprocessing eo-1 hyperion hyperspectral data to support the application of agricultural indexes, IEEE Transactions on Geoscience and Remote Sensing, vol.41, issue.6, pp.1246-1259, 2003.
DOI : 10.1109/TGRS.2003.813206

B. Hörig, F. Kühn, F. Oschütz, and F. Lehmann, HyMap hyperspectral remote sensing to detect hydrocarbons, International Journal of Remote Sensing, vol.22, issue.8, pp.1413-1422, 2001.
DOI : 10.1080/01431160120909

D. Manolakis and G. Shaw, Detection algorithms for hyperspectral imaging applications, IEEE Signal Processing Magazine, vol.19, issue.1, pp.29-43, 2002.
DOI : 10.1109/79.974724

D. W. Stein, S. G. Beaven, L. E. Hoff, E. M. Winter, A. P. 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

M. T. Eismann, A. D. Stocker, and N. M. Nasrabadi, Automated Hyperspectral Cueing for Civilian Search and Rescue, Proceedings of the IEEE, vol.97, issue.6, pp.1031-1055, 2009.
DOI : 10.1109/JPROC.2009.2013561

D. Manolakis, E. Truslow, M. Pieper, T. Cooley, and M. Brueggeman, Detection Algorithms in Hyperspectral Imaging Systems: An Overview of Practical Algorithms, IEEE Signal Processing Magazine, vol.31, issue.1, pp.24-33, 2014.
DOI : 10.1109/MSP.2013.2278915

D. Manolakis, R. Lockwood, T. Cooley, and J. Jacobson, Is there a best hyperspectral detection algorithm, Proc. SPIE 7334, p.733402, 2009.
DOI : 10.1117/2.1200906.1560
URL : http://spie.org/documents/Newsroom/Imported/1560/1560_5724_0_2009-06-15.pdf

J. Frontera-pons, F. Pascal, and J. P. Ovarlez, False-alarm regulation for target detection in hyperspectral imaging, 2013 5th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP), pp.161-164, 2013.
DOI : 10.1109/CAMSAP.2013.6714032

J. Frontera-pons, M. A. Veganzones, S. Velasco-forero, F. Pascal, J. P. Ovarlez et al., Robust anomaly detection in Hyperspectral Imaging, 2014 IEEE Geoscience and Remote Sensing Symposium, pp.4604-4607, 2014.
DOI : 10.1109/IGARSS.2014.6947518
URL : https://hal.archives-ouvertes.fr/hal-01010418

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.

I. S. Reed and X. Yu, Adaptive multiple-band CFAR detection of an optical pattern with unknown spectral distribution, IEEE Transactions on Acoustics, Speech, and Signal Processing, vol.38, issue.10, pp.1760-1770, 1990.
DOI : 10.1109/29.60107

E. J. Kelly, An Adaptive Detection Algorithm, IEEE Transactions on Aerospace and Electronic Systems, vol.22, issue.2, pp.115-127, 1986.
DOI : 10.1109/TAES.1986.310745

C. Chang and S. Chiang, Anomaly detection and classification for hyperspectral imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.40, issue.6, pp.1314-1325, 2002.
DOI : 10.1109/TGRS.2002.800280

J. C. Harsanyi, Detection and classification of subpixel spectral signatures in hyperspectral image sequences, 1993.

J. Frontera-pons, F. Pascal, and J. P. Ovarlez, Adaptive Nonzero-Mean Gaussian Detection, IEEE Transactions on Geoscience and Remote Sensing, vol.55, issue.2, pp.1117-1124, 2017.
DOI : 10.1109/TGRS.2016.2619862
URL : https://hal.archives-ouvertes.fr/hal-01407449

M. J. Pourahmadi-]-p, E. Bickel, and . Levina, Joint mean-covariance models with applications to longitudinal data: unconstrained parameterisation, Biometrika, vol.86, issue.3, pp.677-690, 1999.
DOI : 10.1093/biomet/86.3.677

A. J. Rothman, E. Levina, and J. Zhu, Generalized Thresholding of Large Covariance Matrices, Journal of the American Statistical Association, vol.104, issue.485, pp.177-186, 2009.
DOI : 10.1198/jasa.2009.0101

G. Cao and C. Bouman, Covariance estimation for high dimensional data vectors using the sparse matrix transform, Advances in Neural Information Processing Systems 21, pp.225-232, 2009.

W. B. Wu and M. Pourahmadi, Nonparametric estimation of large covariance matrices of longitudinal data, Biometrika, vol.90, issue.4, p.831, 2003.
DOI : 10.1093/biomet/90.4.831

J. Z. Huang, N. Liu, M. Pourahmadi, and L. Liu, Covariance matrix selection and estimation via penalised normal likelihood, Biometrika, vol.93, issue.1, pp.85-98, 2006.
DOI : 10.1093/biomet/93.1.85

E. J. Kelly, An adaptive detection algorithm Aerospace and Electronic Systems, IEEE Transactions on, vol.23, issue.1, pp.115-127, 1986.

J. Fan and R. Li, Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties, Journal of the American Statistical Association, vol.96, issue.456, pp.1348-1360, 2001.
DOI : 10.1198/016214501753382273

P. Gong, C. Zhang, Z. Lu, J. Huang, and J. Ye, Gist: General iterative shrinkage and thresholding for non-convex sparse learning, 2013.

J. Barzilai and J. M. Borwein, Two-Point Step Size Gradient Methods, IMA Journal of Numerical Analysis, vol.8, issue.1, pp.141-148, 1988.
DOI : 10.1093/imanum/8.1.141

T. Zhang, Analysis of multi-stage convex relaxation for sparse regularization, J. Mach. Learn. Res, vol.11, pp.1081-1107, 2010.

C. Z. Gong and J. Ye, Multi-stage multi-task feature learning, Advances in Neural Information Processing Systems 25, pp.1988-1996, 2012.

E. Candès, M. B. Wakin, and S. P. Boyd, Enhancing Sparsity by Reweighted ??? 1 Minimization, Journal of Fourier Analysis and Applications, vol.7, issue.3, pp.877-905, 2008.
DOI : 10.1007/978-1-4757-4182-7

C. Zhang, Nearly unbiased variable selection under minimax concave penalty, The Annals of Statistics, vol.38, issue.2, pp.894-94209, 2010.
DOI : 10.1214/09-AOS729
URL : http://doi.org/10.1214/09-aos729

J. Frontera-pons, M. A. Veganzones, F. Pascal, and J. P. Ovarlez, Hyperspectral anomaly detectors using robust estimators Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal, vol.9, issue.2, pp.720-731, 2016.
DOI : 10.1109/jstars.2015.2453014

Y. Zhang, B. Du, and L. Zhang, A Sparse Representation-Based Binary Hypothesis Model for Target Detection in Hyperspectral Images, IEEE Transactions on Geoscience and Remote Sensing, vol.53, issue.3, pp.1346-1354, 2015.
DOI : 10.1109/TGRS.2014.2337883