A. Singh, Review Article Digital change detection techniques using remotely-sensed data, Int. J. Remote Sens, vol.10, issue.6, pp.989-1003, 1989.

M. K. Ridd and J. Liu, A comparison of four algorithms for change detection in an urban environment, Remote Sens. Environ, vol.63, issue.2, pp.95-100, 1998.

R. J. Radke, S. Andra, O. Al-kofahi, and B. Roysam, Image change detection algorithms: A systematic survey, IEEE Trans. Image Process, vol.14, issue.3, pp.294-307, 2005.

F. Bovolo and L. Bruzzone, The time variable in data fusion: A change detection perspective, IEEE Geosci. Remote Sens. Mag, vol.3, issue.3, pp.8-26, 2015.

M. Mura, S. Prasad, F. Pacifici, P. Gamba, J. Chanussot et al., Challenges and opportunities of multimodality and data fusion in remote sensing, Proc. IEEE, vol.103, pp.1585-1601, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01259771

D. Landgrebe, Hyperspectral image data analysis, IEEE Signal Process. Mag, vol.19, issue.1, pp.17-28, 2002.

J. B. Campbell and R. H. Wynne, Introduction to Remote Sensing, 2011.

C. Collet, J. Chanussot, and K. Chehdi, Multivariate Image Processing: Methods and Applications, 2006.

C. Elachi and J. J. Van-zyl, Introduction to the Physics and Techniques of Remote Sensing, Series in Remote Sensing and Image Processing), 2006.

J. C. Price, Spectral band selection for visible-near infrared remote sensing: Spectral-spatial resolution tradeoffs, IEEE Trans. Geosci. Remote Sens, vol.35, issue.5, pp.1277-1285, 1997.

F. Bovolo and L. Bruzzone, A theoretical framework for unsupervised change detection based on change vector analysis in the polar domain, IEEE Trans. Geosci. Remote Sens, vol.45, issue.1, pp.218-236, 2007.

F. Bovolo, S. Marchesi, and L. Bruzzone, A framework for automatic and unsupervised detection of multiple changes in multitemporal images, IEEE Trans. Geosci. Remote Sens, vol.50, issue.6, pp.2196-2212, 2012.

A. A. Nielsen, K. Conradsen, and J. J. Simpson, Multivariate alteration detection (MAD) and MAF postprocessing in multispectral, bitemporal image data: New approaches to change detection studies, Remote Sens. Environ, vol.64, issue.1, pp.1-19, 1998.

A. A. Nielsen, The regularized iteratively reweighted MAD method for change detection in multi-and hyperspectral data, IEEE Trans. Image Process, vol.16, issue.2, pp.463-478, 2007.

M. J. Canty, A. A. Nielsen, and M. Schmidt, Automatic radiometric normalization of multitemporal satellite imagery, Remote Sens. Environ, vol.91, pp.441-451, 2004.

J. Inglada and A. Giros, On the possibility of automatic multisensor image registration, IEEE Trans. Geosci. Remote Sens, vol.42, issue.10, pp.2104-2120, 2004.
URL : https://hal.archives-ouvertes.fr/hal-00569950

J. Inglada, Similarity measures for multisensor remote sensing images, Proc. IEEE Int. Conf. Geosci. Remote Sens. (IGARSS), vol.1, pp.104-106, 2002.

V. Alberga, M. Idrissa, V. Lacroix, and J. Inglada, Performance estimation of similarity measures of multi-sensor images for change detection applications, Proc. IEEE Int. Workshop Anal. Multi-Temporal Remote Sens. Images (MultiTemp), pp.1-5, 2007.

G. Mercier, G. Moser, and S. Serpico, Conditional copula for change detection on heterogeneous SAR data, Proc. IEEE Int. Conf. Geosci. Remote Sens. (IGARSS), pp.2394-2397, 2007.

J. Prendes, M. Chabert, F. Pascal, A. Giros, and J. Y. Tourneret, A new multivariate statistical model for change detection in images acquired by homogeneous and heterogeneous sensors, IEEE Trans. Image Process, vol.24, issue.3, pp.799-812, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01127988

J. Prendes, M. Chabert, F. Pascal, A. Giros, and J. Tourneret, Performance assessment of a recent change detection method for homogeneous and heterogeneous images, Revue Française de Photogrammétrie et de Télédétection, vol.209, pp.23-29, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01251780

J. Prendes, M. Chabert, F. Pascal, A. Giros, and J. Tourneret, A Bayesian nonparametric model coupled with a Markov random field for change detection in heterogeneous remote sensing images, SIAM J. Imag. Sci, vol.9, issue.4, pp.1889-1921, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01416149

V. Alberga, M. Idrissa, V. Lacroix, and J. Inglada, Comparison of similarity measures of multi-sensor images for change detection applications, Proc. IEEE Int. Geosci. Remote Sens. Symp. (IGARSS), pp.2358-2361, 2007.

M. N. Klaric, GeoCDX: An automated change detection and exploitation system for high-resolution satellite imagery, IEEE Trans. Geosci. Remote Sens, vol.51, issue.4, pp.2067-2086, 2013.

L. Wald, T. Ranchin, and M. Mangolini, Fusion of satellite images of different spatial resolutions: Assessing the quality of resulting images, Photogramm. Eng. Remote Sens, vol.63, issue.6, pp.691-699, 1997.
URL : https://hal.archives-ouvertes.fr/hal-00365304

L. Loncan, Hyperspectral pansharpening: A review, IEEE Trans. Geosci. Remote Sens, vol.3, issue.3, pp.27-46, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01403205

Q. Wei, J. Bioucas-dias, N. Dobigeon, and J. Y. Tourneret, Hyperspectral and multispectral image fusion based on a sparse representation, IEEE Trans. Geosci. Remote Sens, vol.53, issue.7, pp.3658-3668, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01168121

Q. Wei, N. Dobigeon, and J. Tourneret, Fast fusion of multi-band images based on solving a Sylvester equation, IEEE Trans. Image Process, vol.24, issue.11, pp.4109-4121, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01187314

Q. Wei, N. Dobigeon, and J. Y. Tourneret, Bayesian fusion of multiband images, IEEE J. Sel. Topics Signal Process, vol.9, issue.6, pp.1117-1127, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01187286

N. Yokoya, N. Mayumi, and A. Iwasaki, Cross-calibration for data fusion of EO-1/hyperion and Terra/ASTER, IEEE J. Sel. Topics Appl. Earth Observ. Remote Sens, vol.6, issue.2, pp.419-426, 2013.

F. Heide, FlexISP: A flexible camera image processing framework, ACM Trans. Graph, vol.33, issue.6, 2014.

A. K. Gupta and D. K. Nagar, Matrix Variate Distributions (Monographs and Surveys in Pure and Applied Mathematics), vol.4, 1999.

J. Idier, Bayesian Approach to Inverse Problems (Digital Signal and Image Processing Series, 2008.

R. D. Johnson and E. S. Kasischke, Change vector analysis: A technique for the multispectral monitoring of land cover and condition, Int. J. Remote Sens, vol.19, issue.3, pp.411-426, 1998.

A. Addabbo, G. Satalino, G. Pasquariello, and P. Blonda, Three different unsupervised methods for change detection: An application, Proc. IEEE Int. Conf. Geosci. Remote Sens. (IGARSS), vol.3, pp.1980-1983, 2004.

D. Lu, P. Mausel, E. Brondízio, and E. Moran, Change detection techniques, Int. J. Remote Sens, vol.25, issue.12, pp.2365-2401, 2004.

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

J. M. Nascimento and J. M. Bioucas-dias, Vertex component analysis: A fast algorithm to unmix hyperspectral data, IEEE Trans. Geosci. Remote Sens, vol.43, issue.4, pp.898-910, 2005.

D. C. Heinz and C. Chang, Fully constrained least-squares linear spectral mixture analysis method for material quantification in hyperspectral imagery, IEEE Trans. Geosci. Remote Sens, vol.29, issue.3, pp.529-545, 2001.

V. Ferraris, N. Dobigeon, Q. Wei, and M. Chabert, Detecting changes between optical images of different spatial and spectral resolutions: A fusion-based approach-Complementary results, Dept. Signal Commun., Univ. Toulouse, 2016.

V. Ferraris, N. Dobigeon, Q. Wei, and M. Chabert, Robust fusion of multiband images with different spatial and spectral resolutions for change detection, IEEE Trans. Comput. Imag, vol.3, issue.2, pp.175-186, 2017.