J. Modersitzki, H. Gonçalves, J. Gonçalves, L. Corte-real, A. G. Teodoro3-]-l et al., CHAIR: automatic image registration based on correlation and Hough transformA survey of image registration techniquesImage registration methods: a surveyMutual-information-based registration of medical images: a survey, Mokhtarzade, and H. Ebadi, "Uniform Robust Scale-Invariant Feature Matching for Optical Remote Sensing Images, pp.7936-7968, 1992.

W. Li, H. Leung-zhou, W. Yang, and Q. Liao, A maximum likelihood approach for image registration using control point and intensityA Coarse-to-Fine Subpixel Registration Method to Recover Local Perspective Deformation in the Application of Image Super-Resolution, IEEE Trans. Image Process. IEEE Trans. Image Process, vol.139, issue.21, pp.1115-1127, 2004.

A. Goshtasby and J. Le-moign, Image registration: Principles, tools and methods, 2012.
DOI : 10.1007/978-1-4471-2458-0

I. Karybali, E. Psarakis, K. Berberidis, and G. Evangelidis, Efficient image registration with subpixel accuracy, 14th European Signal Processing Conference (EUSIPCO), 2006.
URL : https://hal.archives-ouvertes.fr/hal-00865121

M. Debella-gilo and A. Kääb, Sub-pixel precision image matching for measuring surface displacements on mass movements using normalized cross-correlation, Remote Sensing of Environment, vol.115, issue.1, pp.130-142, 2011.
DOI : 10.1016/j.rse.2010.08.012

P. Viola and W. W. Iii, Alignment by maximization of mutual information, Proceedings of IEEE International Conference on Computer Vision, pp.137-154, 1997.
DOI : 10.1109/ICCV.1995.466930

A. Collignon, F. Maes, D. Delaere, D. Vandermeulen, P. Suetens et al., Automated multi-modality image registration based on information theory, Information processing in medical imaging, pp.263-274, 1995.

B. W. Silverman, Density estimation for statistics and data analysis. London, 1986.

E. Haber and J. Modersitzki, Intensity gradient based registration and fusion of multimodal images, Methods Inf Med, vol.46, pp.292-301, 2007.

J. Inglada, V. Muron, D. Pichard, and T. Feuvrier, Analysis of Artifacts in Subpixel Remote Sensing Image Registration, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.1, pp.254-264, 2007.
DOI : 10.1109/TGRS.2006.882262
URL : https://hal.archives-ouvertes.fr/hal-00578144

J. P. Pluim, J. B. Antoine-maintz, and M. A. Viergever, Interpolation Artefacts in Mutual Information-Based Image Registration, Computer Vision and Image Understanding, vol.77, issue.2, pp.211-232, 2000.
DOI : 10.1006/cviu.1999.0816

A. Alba, R. Aguilar-ponce, J. Vigueras-gómez, and E. Arce-santana, Phase Correlation Based Image Alignment with Subpixel Accuracy, Advances in Artificial Intelligence, pp.171-182, 2013.
DOI : 10.1007/978-3-642-37807-2_15

P. Thevenaz, U. E. Ruttimann, and M. Unser, A pyramid approach to subpixel registration based on intensity, IEEE Transactions on Image Processing, vol.7, issue.1, pp.27-41, 1998.
DOI : 10.1109/83.650848

D. Robinson and P. Milanfar, Fundamental Performance Limits in Image Registration, IEEE Transactions on Image Processing, vol.13, issue.9, pp.1185-99, 2004.
DOI : 10.1109/TIP.2004.832923

I. S. Yetik and A. Nehorai, Performance bounds on image registration, IEEE Transactions on Signal Processing, vol.54, issue.5, pp.1737-1749, 2006.
DOI : 10.1109/TSP.2006.870552

T. Q. Pham, M. Bezuijen, L. J. Van-vliet, K. Schutte, and C. L. Hendriks, Performance of optimal registration estimators, Visual Information Processing XIV, pp.133-144, 2005.
DOI : 10.1117/12.603304

M. L. Uss, B. Vozel, V. A. Dushepa, V. A. Komjak, and K. Chehdi, A Precise Lower Bound on Image Subpixel Registration Accuracy, IEEE Transactions on Geoscience and Remote Sensing, vol.52, issue.6, pp.3333-3345, 2014.
DOI : 10.1109/TGRS.2013.2272559
URL : https://hal.archives-ouvertes.fr/hal-01109199

J. Meola, M. T. Eismann, R. L. Moses, and J. N. Ash, Modeling and estimation of signal-dependent noise in hyperspectral imagery, Applied Optics, vol.50, issue.21, pp.3829-3846, 2011.
DOI : 10.1364/AO.50.003829

N. Acito, M. Diani, and G. Corsini, Signal-Dependent Noise Modeling and Model Parameter Estimation in Hyperspectral Images, IEEE Transactions on Geoscience and Remote Sensing, vol.49, issue.8, pp.2957-2971, 2011.
DOI : 10.1109/TGRS.2011.2110657

B. Pesquet-popescu and J. L. Vehel, Stochastic fractal models for image processing, IEEE Signal Processing Magazine, vol.19, issue.5, pp.48-62, 2002.
DOI : 10.1109/MSP.2002.1028352
URL : https://hal.archives-ouvertes.fr/inria-00581030

G. D. Martino, A. Iodice, D. Riccio, and G. Ruello, Imaging of Fractal Profiles, IEEE Transactions on Geoscience and Remote Sensing, vol.48, issue.8, pp.3280-3289, 2010.
DOI : 10.1109/TGRS.2010.2044661

S. M. Kay, Fundamentals of statistical signal processing: estimation theory, 1993.

H. Wendt, N. Dobigeon, J. Y. Tourneret, and P. Abry, Bayesian estimation for the multifractality parameter, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.6556-6560, 2013.
DOI : 10.1109/ICASSP.2013.6638929
URL : https://hal.archives-ouvertes.fr/hal-01151027

M. L. Uss, B. Vozel, V. V. Lukin, and K. Chehdi, Local Signal-Dependent Noise Variance Estimation From Hyperspectral Textural Images, IEEE Journal of Selected Topics in Signal Processing, vol.5, issue.3, pp.469-486, 2011.
DOI : 10.1109/JSTSP.2010.2104312
URL : https://hal.archives-ouvertes.fr/hal-00946983

M. L. Uss, B. Vozel, V. V. Lukin, and K. Chehdi, Maximum likelihood estimation of spatially correlated signal-dependent noise in hyperspectral images, Optical Engineering, vol.51, issue.11, pp.111712-111713, 2012.
DOI : 10.1117/1.OE.51.11.111712
URL : https://hal.archives-ouvertes.fr/hal-00802995

D. G. Luenberger and Y. Ye, Linear and nonlinear programming, 2008.
DOI : 10.1007/978-3-319-18842-3

L. Lemieux, R. Jagoe, D. Fish, N. Kitchen, and D. Thomas, A patient-to-computed-tomography image registration method based on digitally reconstructed radiographs, Medical Physics, vol.21, issue.11, pp.1749-1760, 1994.
DOI : 10.1118/1.597276

J. Modersitzki, FAIR: Flexible Algorithms for Image Registration, Philadelphia: SIAM, 2009.
DOI : 10.1137/1.9780898718843

J. R. Schott-hoboken and N. J. , Matrix analysis for statistics, 2005.

H. W. Lilliefors, On the Kolmogorov-Smirnov Test for Normality with Mean and Variance Unknown, Journal of the American Statistical Association, vol.35, issue.318, pp.399-402, 1967.
DOI : 10.1214/aoms/1177728726

M. L. Uss, B. Vozel, V. V. Lukin, and K. Chehdi, Image informative maps for component-wise estimating parameters of signal-dependent noise, Journal of Electronic Imaging, vol.22, issue.1, pp.13019-013019, 2013.
DOI : 10.1117/1.JEI.22.1.013019
URL : https://hal.archives-ouvertes.fr/hal-00905998

J. S. Pearlman, P. S. Barry, C. C. Segal, J. Shepanski, D. Beiso et al., Hyperion, a space-based imaging spectrometer, IEEE Transactions on Geoscience and Remote Sensing, vol.41, issue.6, pp.1160-1173, 2003.
DOI : 10.1109/TGRS.2003.815018

J. R. Irons, J. L. Dwyer, and J. A. Barsi, The next Landsat satellite: The Landsat Data Continuity Mission, Remote Sensing of Environment, pp.11-21, 2012.
DOI : 10.1016/j.rse.2011.08.026

. Hyperion, Landsat 8 and ASTER GDEM images obtained from https://lpdaac.usgs.gov , maintained by the NASA Land Processes Distributed Active Archive Center (LP DAAC) at the USGS, Earth Resources Observation and Science, 2003.

H. Bay, A. Ess, T. Tuytelaars, and L. Van-gool, Speeded-Up Robust Features (SURF), Computer Vision and Image Understanding, vol.110, issue.3, pp.346-359, 2008.
DOI : 10.1016/j.cviu.2007.09.014

M. A. Fischler and R. C. Bolles, Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography, Readings in Computer Vision, pp.726-740, 1987.

H. Goncalves, J. A. Goncalves, and L. Corte, Measures for an Objective Evaluation of the Geometric Correction Process Quality, IEEE Geoscience and Remote Sensing Letters, vol.6, issue.2, pp.292-296, 2009.
DOI : 10.1109/LGRS.2008.2012441

C. Evans, Notes on the OpenSURF Library, 2009.