H. Edward, . Adelson, H. Charles, . Anderson, R. James et al., Pyramid methods in image processing, RCA engineer, vol.29, issue.6, pp.33-41, 1984.

A. Almansa, Echantillonnage, interpolation et détection: applications en imagerie satellitaire, 2002.

A. Almansa, C. Ballester, V. Caselles, and G. Haro, A TV Based Restoration Model with Local Constraints, Journal of Scientific Computing, vol.17, issue.1, pp.209-236, 2008.
DOI : 10.1109/78.80914

F. Ayoub, J. Avouac, A. Lucas, S. Leprince, T. Nathan et al., Measuring mars sand flux seasonality from a time series of hirise images, AGU Fall Meeting Abstracts, p.1873, 2012.

P. Emanuel and . Baltsavias, Airborne laser scanning: existing systems and firms and other resources, ISPRS Journal of Photogrammetry and Remote sensing, vol.54, issue.2, pp.164-198, 1999.

P. Emmanuel and . Baltsavias, Airborne laser scanning: basic relations and formulas. ISPRS Journal of photogrammetry and remote sensing, pp.199-214, 1999.

P. Emmanuel and . Baltsavias, A comparison between photogrammetry and laser scanning, ISPRS Journal of photogrammetry and Remote Sensing, vol.54, issue.2, pp.83-94, 1999.

L. John, . Barron, J. David, . Fleet, S. Steven et al., Performance of optical flow techniques, Computer Vision and Pattern Recognition Proceedings CVPR'92 IEEE Computer Society Conference on, pp.236-242, 1992.

L. Paul, . Basgall, A. Fred, . Kruse, C. Richard et al., Comparison of lidar and stereo photogrammetric point clouds for change detection, SPIE Defense+ Security, pages 90800R?90800R. International Society for Optics and Photonics, 2014.

R. Joshua, . Ben-arie, J. Geoffrey, . Hay, P. Ryan et al., Development of a pit filling algorithm for lidar canopy height models, Computers & Geosciences, vol.35, issue.9, pp.1940-1949, 2009.

R. Bindschadler, Monitoring ice sheet behavior from space, Reviews of Geophysics, vol.246, issue.17, pp.79-104, 1998.
DOI : 10.1126/science.246.4937.1587

S. Birchfield and C. Tomasi, A pixel dissimilarity measure that is insensitive to image sampling, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.20, issue.4, pp.401-406, 1998.
DOI : 10.1109/34.677269

H. Blinchikoff and H. Krause, Filtering in the time and frequency domains, 2001.
DOI : 10.1049/SBEW008E

S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein, Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers, Foundations and Trends?? in Machine Learning, vol.3, issue.1, pp.1-122, 2011.
DOI : 10.1561/2200000016

S. Boyd and L. Vandenberghe, Convex Optimization, 2004.

S. Boyd and L. Vandenberghe, Localization and cutting-plane methods, 2007.

Y. Boykov and V. Kolmogorov, An experimental comparison of min-cut/max- flow algorithms for energy minimization in vision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.9, pp.1124-1137, 2004.
DOI : 10.1109/TPAMI.2004.60

Y. Boykov and O. Veksler, Graph cuts in vision and graphics: Theories and applications. Handbook of mathematical models in computer vision, pp.79-96, 2006.

Y. Boykov, O. Veksler, and R. Zabih, Markov random fields with efficient approximations, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231), pp.648-655, 1998.
DOI : 10.1109/CVPR.1998.698673
URL : http://ecommons.cornell.edu/bitstream/1813/7312/1/97-1658.pdf

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2001.
DOI : 10.1109/34.969114
URL : http://www.csd.uwo.ca/~yuri/Papers/iccv99.pdf

A. Brendon, M. Bradley, and . Cubrinovski, Near-source strong ground motions observed in the 22 february 2011 christchurch earthquake, Seismological Research Letters, vol.82, issue.6, pp.853-865, 2011.

K. Bredies, K. Kunisch, and T. Pock, Total Generalized Variation, SIAM Journal on Imaging Sciences, vol.3, issue.3, pp.492-526, 2010.
DOI : 10.1137/090769521
URL : http://gpu4vision.icg.tugraz.at/papers/2009/pock_tgv.pdf

L. Gottesfeld-brown, A survey of image registration techniques, ACM Computing Surveys, vol.24, issue.4, pp.325-376, 1992.
DOI : 10.1145/146370.146374

A. Buades, B. Coll, and J. Morel, A Review of Image Denoising Algorithms, with a New One, Multiscale Modeling & Simulation, vol.4, issue.2, pp.490-530, 2005.
DOI : 10.1137/040616024
URL : https://hal.archives-ouvertes.fr/hal-00271141

A. Chambolle, An algorithm for total variation minimization and applications, J. Math. Imaging Vis, vol.20, issue.12, pp.89-97, 2004.

A. Chambolle, Total Variation Minimization and a Class of Binary MRF Models, International Workshop on Energy Minimization Methods in Computer Vision and Pattern Recognition, pp.136-152, 2005.
DOI : 10.1007/11585978_10

A. Chambolle and T. Pock, A First-Order Primal-Dual Algorithm for Convex Problems with??Applications to Imaging, Journal of Mathematical Imaging and Vision, vol.60, issue.5, pp.120-145, 2011.
DOI : 10.1007/978-3-540-74936-3_22
URL : https://hal.archives-ouvertes.fr/hal-00490826

F. Tony, S. Chan, M. Esedoglu, and . Nikolova, Algorithms for finding global minimizers of image segmentation and denoising models, SIAM journal on applied mathematics, vol.66, issue.5, pp.1632-1648, 2006.

C. Chen and H. Chu, Similarity measurement between images, Computer Software and Applications Conference, 2005. COMP- SAC 2005. 29th Annual International, pp.41-42, 2005.

E. Gary, . Christensen, J. Hans, and . Johnson, Consistent image registration, IEEE transactions on medical imaging, vol.20, issue.7, pp.568-582, 2001.

A. John, S. Christian, and . Cryan, A survey of lidar technology and its use in spacecraft relative navigation, AIAA Guidance, Navigation, and Control (GNC) Conference, p.4641, 2013.

L. Patrick, J. Combettes, and . Pesquet, A douglas?rachford splitting approach to nonsmooth convex variational signal recovery, IEEE Journal of Selected Topics in Signal Processing, vol.1, issue.4, pp.564-574, 2007.

L. Patrick, J. Combettes, and . Pesquet, Proximal splitting methods in signal processing In Fixed-point algorithms for inverse problems in science and engineering, pp.185-212, 2011.

B. Conejo, . Leprince, J. Ayoub, and . Avouac, Fast global stereo matching via energy pyramid minimization, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, p.41, 2014.
DOI : 10.5194/isprsannals-II-3-41-2014
URL : https://hal.archives-ouvertes.fr/hal-01081593

B. Conejo, N. Komodakis, S. Leprince, and J. P. Avouac, Inference by learning: Speeding-up graphical model optimization via a coarse-to-fine cascade of pruning classifiers, Advances in Neural Information Processing Systems, pp.2105-2113, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01081590

H. Thomas, C. E. Cormen, . Leiserson, L. Ronald, C. Rivest et al., Introduction to algorithms, 2001.

C. Cortes and V. Vapnik, Support vector machine, Machine learning, vol.20, issue.3, pp.273-297, 1995.

B. Costa, T. Battista, and S. Pittman, Comparative evaluation of airborne LiDAR and ship-based multibeam SoNAR bathymetry and intensity for mapping coral reef ecosystems, Remote Sensing of Environment, vol.113, issue.5, pp.1082-1100, 2009.
DOI : 10.1016/j.rse.2009.01.015

R. National and . Council, Earth observations from space: The first 50 years of scientific achievements, 2008.

G. Dantzig, Linear programming and extensions. Princeton university press, 2016.

C. De-franchis, E. Meinhardt-llopis, J. Michel, J. Morel, and G. Facciolo, On stereo-rectification of pushbroom images, 2014 IEEE International Conference on Image Processing (ICIP), pp.5447-5451, 2014.
DOI : 10.1109/ICIP.2014.7026102

C. De-franchis, E. Meinhardt-llopis, J. Michel, J. Morel, and G. Facciolo, An automatic and modular stereo pipeline for pushbroom images, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, p.49, 2014.
DOI : 10.5194/isprsannals-II-3-49-2014
URL : https://hal.archives-ouvertes.fr/hal-01109526

A. Donnellan, B. Hallet, and S. Leprince, Gazing at the solar system: Capturing the evolution of dunes, faults, volcanoes, and ice from space, 2014.

E. Stuart, . Dreyfus, M. Averill, and . Law, Art and Theory of Dynamic Programming, 1977.

D. Roger, . Eastman, S. Nathan, J. L. Netanyahu, and . Moigne, Survey of image registration methods, 2011.

P. Elias, A. Feinstein, and C. Shannon, A note on the maximum flow through a network, IEEE Transactions on Information Theory, vol.2, issue.4, pp.117-119, 1956.
DOI : 10.1109/TIT.1956.1056816

P. Felzenszwalb and D. Huttenlocher, Distance transforms of sampled functions, 2004.

F. Pedro, . Felzenszwalb, P. Daniel, and . Huttenlocher, Efficient belief propagation for early vision, International journal of computer vision, vol.70, issue.1, pp.41-54, 2006.

W. Fenchel, D. W. Blackett, and P. University, Convex cones, sets, and functions, Dept. of Mathematics . Logistics Research Project, 1953.

W. Fenchel, On conjugate convex functions, Journal canadien de math??matiques, vol.1, issue.1, pp.73-77, 1949.
DOI : 10.4153/CJM-1949-007-x

J. Terrence and . Finnegan, Shooting the Front: Allied Aerial Reconnaissance and Photographic Interpretation on the Western Front?World War I. Center for Strategic Intelligence Research National Defense, 2006.

A. Fix, A. Gruber, E. Boros, and R. Zabih, A graph cut algorithm for higher-order Markov Random Fields, 2011 International Conference on Computer Vision, pp.1020-1027, 2011.
DOI : 10.1109/ICCV.2011.6126347

R. Lester, . Ford, R. Delbert, and . Fulkerson, Maximal flow through a network, Canadian journal of Mathematics, vol.8, issue.3, pp.399-404, 1956.

L. Ford, J. , and D. Fulkerson, Maximal flow through a network, Classic papers in combinatorics, pp.243-248, 2009.

G. David and F. , The viterbi algorithm, Proceedings of the IEEE, vol.61, issue.3, pp.268-278, 1973.

J. Friedman, T. Hastie, H. Höfling, and R. Tibshirani, Pathwise coordinate optimization, The Annals of Applied Statistics, vol.1, issue.2, pp.302-332, 2007.
DOI : 10.1214/07-AOAS131
URL : http://doi.org/10.1214/07-aoas131

E. Gamble, T. Poggio, and M. Lab, Visual integration and detection of discontinuities: The key role of intensity edges, 1987.

L. Geli, P. Bard, and B. Jullien, The effect of topography on earthquake ground motion: a review and new results, pp.42-63, 1988.

J. Robert and . Geller, Earthquake prediction: a critical review, Geophysical Journal International, vol.131, issue.3, pp.425-450, 1997.

D. Gilbertson, F. Kent, and . Pyatt, Aerial photography and satellite imagery, Practical Ecology for Geography and Biology, pp.176-193, 1985.
DOI : 10.1007/978-1-4684-1415-8_10

B. Glocker, N. Komodakis, N. Paragios, and N. Navab, Nonrigid registration using discrete mrfs: Application to thoracic ct images, Workshop Evaluation of Methods for Pulmonary Image Registration, 2010.
URL : https://hal.archives-ouvertes.fr/hal-00858382

B. Glocker, N. Komodakis, G. Tziritas, N. Navab, and N. Paragios, Dense image registration through MRFs and efficient linear programming???, Medical Image Analysis, vol.12, issue.6, pp.731-741, 2008.
DOI : 10.1016/j.media.2008.03.006
URL : http://www.mas.ecp.fr/Personnel/nikos/pub/mian08.pdf

X. Michel, . Goemans, P. David, and . Williamson, The primal-dual method for approximation algorithms and its application to network design problems. Approximation algorithms for NP-hard problems, pp.144-191, 1997.

T. Goldstein, M. Li, and X. Yuan, Adaptive primal-dual splitting methods for statistical learning and image processing, Advances in Neural Information Processing Systems 28, pp.2089-2097, 2015.

S. Gosh, History of photogrammetry, 1981.

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

F. Guichard and F. Malgouyres, Total variation based interpolation, Signal Processing Conference 9th European, pp.1-4, 1998.

P. Ravi and . Gupta, Remote sensing geology, 2013.

S. Gupta, A review and comprehensive comparison of image denoising techniques, 2014 International Conference on Computing for Sustainable Global Development (INDIACom), pp.972-976, 2014.
DOI : 10.1109/IndiaCom.2014.6828109

R. Gutierrez, A. Neuenschwander, and M. M. Crawford, Development of laser waveform digitization for airborne LIDAR topographic mapping instrumentation, Proceedings. 2005 IEEE International Geoscience and Remote Sensing Symposium, 2005. IGARSS '05., pp.1154-1157, 2005.
DOI : 10.1109/IGARSS.2005.1525321

J. Hagenauer and P. Hoeher, A viterbi algorithm with softdecision outputs and its applications, Global Telecommunications Conference and Exhibition'Communications Technology for the 1990s and Beyond'(GLOBECOM), pp.1680-1686, 1989.

B. Hallert, Photogrammetry, basic principles and general survey, TRIS, 1960.

T. Harris and F. Ross, Fundamentals of a method for evaluating rail net capacities, 1955.

S. Harsdorf, M. Janssen, R. Reuter, S. Toeneboen, B. Wachowicz et al., Submarine lidar for seafloor inspection, Measurement Science and Technology, vol.10, issue.12, p.1178, 1999.
DOI : 10.1088/0957-0233/10/12/309

R. Hartley and A. Zisserman, Multiple view geometry in computer vision, 2003.
DOI : 10.1017/CBO9780511811685

J. Hervás, I. José, . Barredo, L. Paul, A. Rosin et al., Monitoring landslides from optical remotely sensed imagery: the case history of Tessina landslide, Italy, Geomorphology, vol.54, issue.1-2, pp.63-75, 2003.
DOI : 10.1016/S0169-555X(03)00056-4

J. B. Hiriart-urruty and C. Lemaréchal, Fundamentals of Convex Analysis. Grundlehren Text Editions, 2004.

H. Hirschmuller, Accurate and efficient stereo processing by semiglobal matching and mutual information, Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, pp.807-814, 2005.

E. Honkavaara, R. Arbiol, L. Markelin, L. Martinez, M. Cramer et al., Digital Airborne Photogrammetry???A New Tool for Quantitative Remote Sensing????A State-of-the-Art Review On Radiometric Aspects of Digital Photogrammetric Images, Remote Sensing, vol.62, issue.3, pp.577-605, 2009.
DOI : 10.14358/PERS.75.2.193

Y. Hu and M. Jacob, Higher Degree Total Variation (HDTV) Regularization for Image Recovery, IEEE Transactions on Image Processing, vol.21, issue.5, pp.2559-2571, 2012.
DOI : 10.1109/TIP.2012.2183143

R. David, K. Hunter, and . Lange, A tutorial on mm algorithms. The American Statistician, pp.30-37, 2004.

L. Igual, J. Preciozzi, L. Garrido, A. Almansa, V. Caselles et al., Automatic low baseline stereo in urban areas, Inverse Problems and Imaging, vol.1, issue.2, 2007.

M. Irwan, F. Kimata, K. Hirahara, T. Sagiya, and A. Yamagiwa, Measuring ground deformations with 1-hz gps data: the 2003 tokachi-oki earthquake (preliminary report). Earth, planets and space, pp.389-393, 2004.

H. Ishikawa, Higher-order gradient descent by fusion-move graph cut, 2009 IEEE 12th International Conference on Computer Vision, pp.568-574, 2009.
DOI : 10.1109/ICCV.2009.5459187

M. Jaboyedoff, P. Horton, and M. Derron, Céline Longchamp, and Clément Michoud. Monitoring natural hazards, Encyclopedia of Natural Hazards, pp.686-696, 2013.

O. Jamri?ka, S. Daniel, and A. Hornung, Cache-efficient graph cuts on structured grids, 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp.3673-3680, 2012.
DOI : 10.1109/CVPR.2012.6248113

L. Jones, Monitoring landslides in hazardous terrain using terrestrial LiDAR: an example from Montserrat, Quarterly Journal of Engineering Geology and Hydrogeology, vol.39, issue.4, pp.371-373, 2006.
DOI : 10.1144/1470-9236/06-009

J. Kappes, B. Andres, F. Hamprecht, C. Schnorr, S. Nowozin et al., A comparative study of modern inference techniques for discrete energy minimization problems, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp.1328-1335, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00865699

J. Kim, Visual correspondence using energy minimization and mutual information, Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, pp.1033-1040, 2003.

P. Kohli, . Kumar, H. Philip, and . Torr, P3 & Beyond: Solving Energies with Higher Order Cliques, 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2007.
DOI : 10.1109/CVPR.2007.383204

V. Kolmogorov, Convergent Tree-Reweighted Message Passing for Energy Minimization, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.10, pp.1568-1583, 2006.
DOI : 10.1109/TPAMI.2006.200

V. Kolmogorov and Y. Boykov, Bk-maxflow code

V. Kolmogorov and R. Zabin, What energy functions can be minimized via graph cuts? IEEE transactions on pattern analysis and machine intelligence, pp.147-159, 2004.

N. Komodakis, Fastpd mrf optimization code

N. Komodakis, Towards More Efficient and Effective LP-Based Algorithms for MRF Optimization, Computer Vision?ECCV, pp.520-534, 2010.
DOI : 10.1007/978-3-642-15552-9_38

N. Komodakis, N. Paragios, and G. Tziritas, MRF Optimization via Dual Decomposition: Message-Passing Revisited, 2007 IEEE 11th International Conference on Computer Vision, pp.1-8, 2007.
DOI : 10.1109/ICCV.2007.4408890
URL : http://www.mas.ecp.fr/Personnel/nikos/pub/iccv07.pdf

N. Komodakis, N. Paragios, and G. Tziritas, MRF Energy Minimization and Beyond via Dual Decomposition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.33, issue.3, pp.531-552, 2011.
DOI : 10.1109/TPAMI.2010.108
URL : https://hal.archives-ouvertes.fr/hal-00856311

N. Komodakis and G. Tziritas, A new framework for approximate labeling via graph cuts, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1, pp.1018-1025, 2005.
DOI : 10.1109/ICCV.2005.14

N. Komodakis and G. Tziritas, Approximate Labeling via Graph Cuts Based on Linear Programming, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.29, issue.8, pp.1436-1453, 2007.
DOI : 10.1109/TPAMI.2007.1061

N. Komodakis, G. Tziritas, and N. Paragios, Performance vs computational efficiency for optimizing single and dynamic MRFs: Setting the state of the art with primal-dual strategies, Computer Vision and Image Understanding, vol.112, issue.1, pp.14-29, 2008.
DOI : 10.1016/j.cviu.2008.06.007
URL : https://hal.archives-ouvertes.fr/hal-00918699

K. Kovari, General report: methods of monitoring landslides, Proceedings of the Fifth International Symposium on Landslides, pp.10-15

F. John, A. S. Kreis, . Cochran-jr, C. Robert, . Ehrhart et al., Piercing the fog: Intelligence and army air forces operations in world war 2, 1996.

S. Krishnan, C. Baru, and C. Crosby, Evaluation of MapReduce for Gridding LIDAR Data, 2010 IEEE Second International Conference on Cloud Computing Technology and Science, pp.33-40, 2010.
DOI : 10.1109/CloudCom.2010.34

T. Donald, . Lauer, A. Stanley, . Morain, V. Vincent et al., The landsat program: Its origins, evolution, and impacts, Photogrammetric Engineering and Remote Sensing, vol.63, issue.7, pp.831-838, 1997.

L. Eugene and . Lawler, Combinatorial optimization: networks and matroids, Courier Corporation, 2001.

F. Leberl, . Gruber, . Ponticelli, R. Bernoegger, and . Perko, The ultracam large format aerial digital camera system, Proceedings of the American Society For Photogrammetry & Remote Sensing, pp.5-9, 2003.

C. Lemaréchal and C. Sagastizábal, Practical Aspects of the Moreau--Yosida Regularization: Theoretical Preliminaries, SIAM Journal on Optimization, vol.7, issue.2, pp.367-385, 1997.
DOI : 10.1137/S1052623494267127

M. Lemmens, A survey on stereo matching techniques. International Archives of Photogrammetry and Remote Sensing, pp.11-23, 1988.

S. Leprince, F. Ayoub, Y. Klinger, and J. Avouac, Co-registration of optically sensed images and correlation (cosicorr ): An operational methodology for ground deformation measurements, Geoscience and Remote Sensing Symposium, pp.1943-1946, 2007.

P. John and . Lewis, Fast normalized cross-correlation, Vision interface, pp.120-123, 1995.

J. Liu and J. Sun, Parallel graph-cuts by adaptive bottom-up merging, Computer Vision and Pattern Recognition 2010 IEEE Conference on, pp.2181-2188, 2010.

T. Luhmann, A historical review on panorama photogrammetry. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, p.8, 2004.

F. Malgouyres, Combining total variation and wavelet packet approaches for image deblurring, Proceedings IEEE Workshop on Variational and Level Set Methods in Computer Vision, pp.57-64, 2001.
DOI : 10.1109/VLSM.2001.938882

F. Malgouyres, Mathematical analysis of a model which combines total variation and wavelet for image restoration, Journal of information processes, vol.2, issue.1, pp.1-10, 2002.

F. Malgouyres, Minimizing the total variation under a general convex constraint for image restoration, IEEE Transactions on Image Processing, vol.11, issue.12, pp.1450-1456, 2002.
DOI : 10.1109/TIP.2002.806241

F. André, . Martins, A. Mário, . Figueiredo, M. Pedro et al., Ad3: alternating directions dual decomposition for map inference in graphical models, Journal of Machine Learning Research, vol.16, pp.495-545, 2015.

D. Francis and M. , Digital elevation model technologies and applications: the DEM users manual, 2007.

S. Mcdougall and O. Hungr, Dynamic modelling of entrainment in rapid landslides, Canadian Geotechnical Journal, vol.392, issue.11, pp.1437-1448, 2005.
DOI : 10.1017/S0022112099005467

S. Alfred, . Mcewen, M. Eric, . Eliason, W. James et al., Mars reconnaissance orbiter's high resolution imaging science experiment (hirise), Journal of Geophysical Research: Planets, issue.E5, p.112, 2007.

P. Meixner and M. Eckstein, Multi-temporal analysis of wwii reconnaissance photos. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.973-978, 2016.

M. Louis and . Milne-thomson, The calculus of finite differences, 1933.

D. Renato, I. Monteiro, and . Adler, Interior path following primal-dual algorithms. part i: Linear programming, Mathematical programming, vol.44, issue.1, pp.27-41, 1989.

J. Moreau, Propriétés des applications " prox, CR Acad. Sci. Paris, vol.256, pp.1069-1071, 1963.

J. Moreau, Proximit?? et dualit?? dans un espace hilbertien, Bulletin de la Société mathématique de France, vol.79, issue.2, pp.273-299, 1965.
DOI : 10.24033/bsmf.1625

C. Mukesh, . Motwani, C. Mukesh, . Gadiya, C. Rakhi et al., Survey of image denoising techniques, Proceedings of GSPX, 2004.

P. Kevin, Y. Murphy, . Weiss, I. Michael, and . Jordan, Loopy belief propagation for approximate inference: An empirical study, Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence, pp.467-475, 1999.

L. George, L. Nemhauser, C. Wiley, . Nemhauser, G. Savelsbergh et al., Integer programming and combinatorial optimization Constraint Classification for Mixed Integer Programming Formulations, COAL Bulletin, vol.20, pp.8-12, 1988.

. Yu and . Nesterov, Smooth minimization of nonsmooth functions, Math. Programming, pp.127-152, 2005.

M. Neteler and H. Mitasova, Open source GIS: a GRASS GIS approach, 2013.

M. Nikolova and . Optimization, Applications in image processing, 2016.

N. Jpl, High resolution imaging science experi- ment

J. Oh, Novel Approach to Epipolar Resampling of HRSI and Satellite Stereo Imagery-based Georeferencing of Aerial Images, 2011.

S. Oh, H. Woo, S. Yun, and M. Kang, Non-convex hybrid total variation for image denoising, Journal of Visual Communication and Image Representation, vol.24, issue.3, pp.332-344, 2013.
DOI : 10.1016/j.jvcir.2013.01.010

Y. Okada, Surface deformation due to shear and tensile faults in a half-space, Bulletin of the seismological society of America, vol.75, issue.4, pp.1135-1154, 1985.

P. Francisco, J. Oliveira, R. Manuel, and . Tavares, Medical image registration: a review Computer methods in biomechanics and biomedical engineering, pp.73-93, 2014.

H. Christos, K. Papadimitriou, and . Steiglitz, Combinatorial optimization: algorithms and complexity, Courier Corporation, 1982.

N. Parikh and S. Boyd, Proximal Algorithms, Foundations and Trends?? in Optimization, vol.1, issue.3, pp.127-239, 2014.
DOI : 10.1561/2400000003
URL : http://www.nowpublishers.com/article/DownloadSummary/OPT-003

R. Gary, P. Ronald, and L. Rardin, Discrete optimization, 2014.

T. Pock and A. Chambolle, Diagonal preconditioning for first order primal-dual algorithms in convex optimization, 2011 International Conference on Computer Vision, pp.1762-1769, 2011.
DOI : 10.1109/ICCV.2011.6126441

C. Sorin and . Popescu, Lidar remote sensing Advances in environmental remote sensing: Sensors, algorithms, and applications, pp.57-84, 2011.

R. T. Rockafellar, Convex Analysis. Princeton landmarks in mathematics and physics, 1970.

A. Rosu, M. Pierrot-deseilligny, A. Delorme, R. Binet, and Y. Klinger, Measurement of ground displacement from optical satellite image correlation using the free open-source software MicMac, ISPRS Journal of Photogrammetry and Remote Sensing, vol.100, pp.48-59, 2015.
DOI : 10.1016/j.isprsjprs.2014.03.002

T. Roughgarden, Lecture 2: Augmenting path algorithms for maximum flow, 2016.

I. Leonid, S. Rudin, E. Osher, and . Fatemi, Nonlinear total variation based noise removal algorithms, Physica D, vol.60, issue.1-4, pp.259-268, 1992.

N. Sabater, A. Almansa, and J. Morel, Meaningful Matches in Stereovision, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.5, pp.930-942, 2012.
DOI : 10.1109/TPAMI.2011.207
URL : https://hal.archives-ouvertes.fr/hal-00647995

N. Sabater, J. Morel, and A. Almansa, How Accurate Can Block Matches Be in Stereo Vision?, SIAM Journal on Imaging Sciences, vol.4, issue.1, pp.472-500, 2011.
DOI : 10.1137/100797849
URL : https://hal.archives-ouvertes.fr/hal-00671759

D. Scherler, S. Leprince, and M. R. Strecker, Glacier-surface velocities in alpine terrain from optical satellite imagery???Accuracy improvement and quality assessment, Remote Sensing of Environment, vol.112, issue.10, pp.3806-3819, 2008.
DOI : 10.1016/j.rse.2008.05.018

M. Schmidt and K. Alahari, Generalized fast approximate energy minimization via graph cuts: Alpha-expansion beta-shrink moves, 2011.
URL : https://hal.archives-ouvertes.fr/inria-00617524

A. Schrijver, Combinatorial optimization: polyhedra and efficiency, 2003.

A. Schwalb, The tiros-n/noaa ag satellite series, NASA STI/Recon Technical Report N, p.79, 1978.

S. Silvestro, . Lk-fenton, . Da-vaz, G. Bridges, and . Ori, Ripple migration and dune activity on Mars: Evidence for dynamic wind processes, Geophysical Research Letters, vol.27, issue.8, p.37, 2010.
DOI : 10.1029/1999GL008399

A. Sotiras, C. Davatzikos, and N. Paragios, Deformable Medical Image Registration: A Survey, IEEE Transactions on Medical Imaging, vol.32, issue.7, pp.1153-1190, 2013.
DOI : 10.1109/TMI.2013.2265603
URL : https://hal.archives-ouvertes.fr/hal-00858737

A. C. Sparavigna, A Study of Moving Sand Dunes by Means of Satellite Images, International Journal of Sciences, vol.1, issue.08, 2013.
DOI : 10.18483/ijSci.229

B. St-onge, C. Vega, Y. Fournier, and . Hu, Mapping canopy height using a combination of digital stereo???photogrammetry and lidar, International Journal of Remote Sensing, vol.72, issue.11, pp.293343-3364, 2008.
DOI : 10.5589/m03-032

S. Sternberg, G. William, and . Stroud, Tiros i-meteorological satellite, 1960.

P. Sturm, Pinhole Camera Model, Computer Vision, pp.610-613, 2014.
DOI : 10.1007/978-0-387-31439-6_472
URL : https://hal.archives-ouvertes.fr/hal-00794084

R. Szeliski, R. Zabih, D. Scharstein, O. Veksler, V. Kolmogorov et al., A Comparative Study of Energy Minimization Methods for Markov Random Fields with Smoothness-Based Priors, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.30, issue.6, pp.1068-1080, 2008.
DOI : 10.1109/TPAMI.2007.70844

R. Takapoui and H. Javadi, Preconditioning via Diagonal Scaling. ArXiv e-prints, 2016.

C. Charles and . Taylor, Measures of similarity between two images. Lecture Notes-Monograph Series, pp.382-391, 1991.

J. Thurston, How does satellite imagery compare with aerial photography. The European Association of, 2010.

O. Veksler and A. Delong, Alpha expansion code

T. Verma and D. Batra, MaxFlow Revisited: An Empirical Comparison of Maxflow Algorithms for Dense Vision Problems, Procedings of the British Machine Vision Conference 2012, pp.1-12, 2012.
DOI : 10.5244/C.26.61

K. Robert and . Vincent, Fundamentals of geological and environmental remote sensing, NJ, vol.366, 1997.

P. Viola, M. William, and . Wells, Alignment by maximization of mutual information, Computer Vision Proceedings., Fifth International Conference on, pp.16-23, 1995.

J. David, . Wald, C. Bruce, V. Worden, K. L. Quitoriano et al., Shakemap manual: technical manual, user's guide, and software guide, 2005.

M. Werlberger, T. Pock, and H. Bischof, Motion estimation with non-local total variation regularization, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.2464-2471, 2010.
DOI : 10.1109/CVPR.2010.5539945
URL : http://lmb.informatik.uni-freiburg.de/lectures/seminar_brox/seminar_ws1011/cvpr10_werlberger.pdf

S. Jonathan, . Yedidia, T. William, Y. Freeman, and . Weiss, Generalized belief propagation, Advances in neural information processing systems, pp.689-695, 2001.

J. Yoo and T. Han, Fast normalized cross-correlation. Circuits , Systems, and Signal Processing, pp.819-843, 2009.
DOI : 10.1007/s00034-009-9130-7

J. Yuan, E. Bae, and X. Tai, A study on continuous max-flow and min-cut approaches, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.2217-2224, 2010.
DOI : 10.1109/CVPR.2010.5539903

J. Yuan, E. Bae, and X. Tai, A study on continuous max-flow and min-cut approaches, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.2217-2224, 2010.
DOI : 10.1109/CVPR.2010.5539903

R. Zabih and J. Woodfill, Non-parametric local transforms for computing visual correspondence, European conference on computer vision, pp.151-158, 1994.
DOI : 10.1007/BFb0028345

S. Zagoruyko and N. Komodakis, Learning to compare image patches via convolutional neural networks, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.4353-4361, 2015.
DOI : 10.1109/CVPR.2015.7299064
URL : https://hal.archives-ouvertes.fr/hal-01246261

M. Zhu and T. Chan, An efficient primal-dual hybrid gradient algorithm for total variation image restoration, UCLA CAM Report, vol.34, 2008.

B. Zitova and J. Flusser, Image registration methods: a survey, Image and Vision Computing, vol.21, issue.11, pp.977-1000, 2003.
DOI : 10.1016/S0262-8856(03)00137-9