Q. Wei, N. Dobigeon, and J. Tourneret, Bayesian fusion of hyperspectral and multispectral images, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp.69-72, 2014.
DOI : 10.1109/ICASSP.2014.6854186
URL : https://hal.archives-ouvertes.fr/hal-01150337

I. Amro, J. Mateos, M. Vega, R. Molina, and A. K. Katsaggelos, A survey of classical methods and new trends in pansharpening of multispectral images, EURASIP Journal on Advances in Signal Processing, vol.2011, issue.1, pp.1-22, 2011.
DOI : 10.1080/01431160412331330239

M. Joshi and A. Jalobeanu, MAP Estimation for Multiresolution Fusion in Remotely Sensed Images Using an IGMRF Prior Model, IEEE Transactions on Geoscience and Remote Sensing, vol.48, issue.3
DOI : 10.1109/TGRS.2009.2030323

X. Ding, Y. Jiang, Y. Huang, and J. Paisley, Pan-sharpening with a Bayesian nonparametric dictionary learning model, Proc. Int. Conf

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

K. Kotwal and S. Chaudhuri, A Bayesian approach to visualization-oriented hyperspectral image fusion, Information Fusion, vol.14, issue.4, pp.349-360, 2013.
DOI : 10.1016/j.inffus.2013.02.007

M. Cetin and N. Musaoglu, Merging hyperspectral and panchromatic image data: qualitative and quantitative analysis, International Journal of Remote Sensing, vol.62, issue.7, pp.1779-1804, 2009.
DOI : 10.1080/014311698215973

G. A. Licciardi, M. M. Khan, J. Chanussot, A. Montanvert, L. Condat et al., Fusion of hyperspectral and panchromatic images using multiresolution analysis and nonlinear PCA band reduction, EURASIP J. Adv. Signal Process, vol.2012, issue.1, pp.1-17, 2012.
URL : https://hal.archives-ouvertes.fr/hal-00696054

L. Loncan, Introducing hyperspectral pansharpening, IEEE Geosci. Remote Sens. Mag, 2015.

M. Moeller, T. Wittman, and A. L. Bertozzi, A variational approach to hyperspectral image fusion, Algorithms and Technologies for Multispectral, Hyperspectral, and Ultraspectral Imagery XV, 2009.
DOI : 10.1117/12.818243

]. Z. Chen, H. Pu, B. Wang, and G. Jiang, Fusion of Hyperspectral and Multispectral Images: A Novel Framework Based on Generalization of Pan-Sharpening Methods, IEEE Geoscience and Remote Sensing Letters, vol.11, issue.8, pp.1418-1422, 2014.
DOI : 10.1109/LGRS.2013.2294476

M. E. Winter and E. Winter, Resolution enhancement of hyperspectral data, Proceedings, IEEE Aerospace Conference, pp.3-1523, 2002.
DOI : 10.1109/AERO.2002.1035290

G. Chen, S. Qian, J. Ardouin, and W. Xie, SUPER-RESOLUTION OF HYPERSPECTRAL IMAGERY USING COMPLEX RIDGELET TRANSFORM, International Journal of Wavelets, Multiresolution and Information Processing, vol.10, issue.03, pp.1-22, 2012.
DOI : 10.1142/S0219691312500257

N. Ohgi, A. Iwasaki, T. Kawashima, and H. Inada, Japanese hypermulti spectral mission, Proc. IEEE Int. Conf. Geosci. Remote Sens. (IGARSS), pp.3756-3759, 2010.

N. Yokoya and A. Iwasaki, Hyperspectral and multispectral data fusion mission on hyperspectral imager suite (HISUI), 2013 IEEE International Geoscience and Remote Sensing Symposium, IGARSS, pp.4086-4089, 2013.
DOI : 10.1109/IGARSS.2013.6723731

J. Zhou, D. Civco, and J. Silander, A wavelet transform method to merge Landsat TM and SPOT panchromatic data, International Journal of Remote Sensing, vol.19, issue.4, pp.743-757, 1998.
DOI : 10.1080/014311698215973

M. González-audícana, J. L. Saleta, R. G. Catalán, and R. García, Fusion of multispectral and panchromatic images using improved IHS and PCA mergers based on wavelet decomposition, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.6, pp.1291-1299, 2004.
DOI : 10.1109/TGRS.2004.825593

R. C. Hardie, M. T. Eismann, and G. L. Wilson, MAP Estimation for Hyperspectral Image Resolution Enhancement Using an Auxiliary Sensor, IEEE Transactions on Image Processing, vol.13, issue.9, pp.1174-1184, 2004.
DOI : 10.1109/TIP.2004.829779

Y. Zhang, S. De-backer, and P. Scheunders, Noise-Resistant Wavelet-Based Bayesian Fusion of Multispectral and Hyperspectral Images, IEEE Transactions on Geoscience and Remote Sensing, vol.47, issue.11, pp.3834-3843, 2009.
DOI : 10.1109/TGRS.2009.2017737

M. T. Eismann and R. C. Hardie, Application of the stochastic mixing model to hyperspectral resolution enhancement, IEEE Transactions on Geoscience and Remote Sensing, vol.42, issue.9, pp.1924-1933, 2004.
DOI : 10.1109/TGRS.2004.830644

M. T. Eismann and R. C. Hardie, Hyperspectral resolution enhancement using high-resolution multispectral imagery with arbitrary response functions, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.3, pp.455-465, 2005.
DOI : 10.1109/TGRS.2004.837324

X. Otazu, M. Gonzalez-audicana, O. Fors, and J. Nunez, Introduction of sensor spectral response into image fusion methods. Application to wavelet-based methods, IEEE Transactions on Geoscience and Remote Sensing, vol.43, issue.10, pp.2376-2385, 2005.
DOI : 10.1109/TGRS.2005.856106

N. Dobigeon, S. Moussaoui, M. Coulon, J. Tourneret, and A. O. Hero, Joint Bayesian Endmember Extraction and Linear Unmixing for Hyperspectral Imagery, IEEE Transactions on Signal Processing, vol.57, issue.11, pp.4355-4368, 2009.
DOI : 10.1109/TSP.2009.2025797
URL : https://hal.archives-ouvertes.fr/hal-00548758

M. V. Joshi, L. Bruzzone, and S. Chaudhuri, A Model-Based Approach to Multiresolution Fusion in Remotely Sensed Images, IEEE Transactions on Geoscience and Remote Sensing, vol.44, issue.9
DOI : 10.1109/TGRS.2006.873340

C. P. Robert and G. Casella, Monte Carlo Statistical Methods, 2004.

S. Duane, A. D. Kennedy, B. J. Pendleton, and D. Roweth, Hybrid Monte Carlo, Physics Letters B, vol.195, issue.2, pp.216-222, 1987.
DOI : 10.1016/0370-2693(87)91197-X

R. M. Neal, MCMC using Hamiltonian dynamics Handbook of Markov Chain Monte Carlo, pp.113-162, 2010.

D. Fasbender, D. Tuia, P. Bogaert, and M. Kanevski, Support-Based Implementation of Bayesian Data Fusion for Spatial Enhancement: Applications to ASTER Thermal Images, IEEE Geoscience and Remote Sensing Letters, vol.5, issue.4, pp.598-602, 2008.
DOI : 10.1109/LGRS.2008.2000739

M. Elbakary and M. Alam, Superresolution Construction of Multispectral Imagery Based on Local Enhancement, IEEE Geoscience and Remote Sensing Letters, vol.5, issue.2, pp.276-279, 2008.
DOI : 10.1109/LGRS.2008.915935

J. B. Campbell, Introduction to remote sensing, Geocarto International, vol.2, issue.4, 2002.
DOI : 10.1080/10106048709354126

R. Schultz and R. Stevenson, A Bayesian approach to image expansion for improved definition, IEEE Transactions on Image Processing, vol.3, issue.3, pp.233-242, 1994.
DOI : 10.1109/83.287017

J. M. Bioucas-dias and J. M. Nascimento, Hyperspectral Subspace Identification, IEEE Transactions on Geoscience and Remote Sensing, vol.46, issue.8, pp.2435-2445, 2008.
DOI : 10.1109/TGRS.2008.918089
URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.304.2548

Q. Wei, N. Dobigeon, and J. Tourneret, Bayesian Fusion of Multi-Band Images?Complementary Results and Supporting Materials Univ, 2014.

R. C. Hardie, K. J. Barnard, and E. E. Armstrong, Joint MAP registration and high-resolution image estimation using a sequence of undersampled images, IEEE Transactions on Image Processing, vol.6, issue.12, pp.1621-1633, 1997.
DOI : 10.1109/83.650116

N. A. Woods, N. P. Galatsanos, and A. K. Katsaggelos, Stochastic methods for joint registration, restoration, and interpolation of multiple undersampled images, IEEE Transactions on Image Processing, vol.15, issue.1, pp.201-213, 2006.
DOI : 10.1109/TIP.2005.860355

E. Punskaya, C. Andrieu, A. Doucet, and W. Fitzgerald, Bayesian curve fitting using MCMC with applications to signal segmentation, IEEE Transactions on Signal Processing, vol.50, issue.3, pp.747-758, 2002.
DOI : 10.1109/78.984776

C. P. Robert, The Bayesian Choice: From Decision-Theoretic Motivations to Computational Implementation, ser. Springer Texts in Statistics, 2007.
DOI : 10.1007/978-1-4757-4314-2

M. Bouriga and O. Féron, Estimation of covariance matrices based on hierarchical inverse-Wishart priors, Journal of Statistical Planning and Inference, vol.143, issue.4, pp.795-808, 2013.
DOI : 10.1016/j.jspi.2012.09.006

R. M. Neal, Probabilistic inference using Markov chain Monte Carlo methods, Dept. of Comput. Sci., Univ. of Toronto, 1993.

H. Zhang, Y. Zhang, H. Li, and T. S. Huang, Generative Bayesian Image Super Resolution With Natural Image Prior, IEEE Transactions on Image Processing, vol.21, issue.9, pp.4054-4067, 2012.
DOI : 10.1109/TIP.2012.2199330

F. Orieux, O. Féron, and J. Giovannelli, Sampling High-Dimensional Gaussian Distributions for General Linear Inverse Problems, IEEE Signal Processing Letters, vol.19, issue.5, pp.251-254, 2012.
DOI : 10.1109/LSP.2012.2189104
URL : https://hal.archives-ouvertes.fr/hal-00779449

S. T. Roweis and L. K. Saul, Nonlinear Dimensionality Reduction by Locally Linear Embedding, Science, vol.290, issue.5500, pp.2323-2326, 2000.
DOI : 10.1126/science.290.5500.2323
URL : http://astro.temple.edu/~msobel/courses_files/saulmds.pdf

J. Wang and C. Chang, Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis, IEEE Transactions on Geoscience and Remote Sensing, vol.44, issue.6, pp.1586-1600, 2006.
DOI : 10.1109/TGRS.2005.863297

R. Dianat and S. Kasaei, Dimension Reduction of Optical Remote Sensing Images via Minimum Change Rate Deviation Method, IEEE Transactions on Geoscience and Remote Sensing, vol.48, issue.1, pp.198-206, 2010.
DOI : 10.1109/TGRS.2009.2024306

G. O. Roberts and J. S. Rosenthal, Coupling and Ergodicity of Adaptive Markov Chain Monte Carlo Algorithms, Journal of Applied Probability, vol.44, issue.02, pp.458-475, 2007.
DOI : 10.1214/ss/1015346320

Y. Zhang, A. Duijster, and P. Scheunders, A Bayesian Restoration Approach for Hyperspectral Images, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.9, pp.3453-3462, 2012.
DOI : 10.1109/TGRS.2012.2184122

N. Yokoya, T. Yairi, and A. Iwasaki, Coupled Nonnegative Matrix Factorization Unmixing for Hyperspectral and Multispectral Data Fusion, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.2, pp.528-537, 2012.
DOI : 10.1109/TGRS.2011.2161320

Z. Wang and A. C. Bovik, A universal image quality index, IEEE Signal Processing Letters, vol.9, issue.3, pp.81-84, 2002.
DOI : 10.1109/97.995823

L. Wald, Quality of high resolution synthesised images: Is there a simple criterion?, Proc. Int. Conf. Fusion of Earth Data, pp.99-103, 2000.
URL : https://hal.archives-ouvertes.fr/hal-00395027

Y. Tarabalka, M. Fauvel, J. Chanussot, and J. Benediktsson, SVM- and MRF-Based Method for Accurate Classification of Hyperspectral Images, IEEE Geoscience and Remote Sensing Letters, vol.7, issue.4, pp.736-740, 2010.
DOI : 10.1109/LGRS.2010.2047711
URL : https://hal.archives-ouvertes.fr/hal-00578864

S. Rahmani, M. Strait, D. Merkurjev, M. Moeller, and T. Wittman, An Adaptive IHS Pan-Sharpening Method, IEEE Geoscience and Remote Sensing Letters, vol.7, issue.4, pp.746-750, 2010.
DOI : 10.1109/LGRS.2010.2046715

Q. Wei, J. M. Bioucas-dias, N. Dobigeon, and J. Tourneret, Hyperspectral and Multispectral Image Fusion Based on a Sparse Representation, IEEE Transactions on Geoscience and Remote Sensing, vol.53, issue.7, 2015.
DOI : 10.1109/TGRS.2014.2381272
URL : https://hal.archives-ouvertes.fr/hal-01168121