O. Mougin, Implémentation d'algorithmes d'interpolation par B-splines (4 mois, 2005.

R. Moulen, Reconstruction tridimensionnelle par déconvolution automatique en microscopie cellulaire (6 mois), 2004.

K. Haddadou, Segmentation des anévrismes de l'aorte dans des images scanner X (6 mois), 2002.

J. Grazzini, Étude des trajectoires et de la déformation de structures nuageuses dans des séquences d'images satellites (6 mois), 2000.

D. Béréziat, I. Herlin, and L. Younes, Motion estimation using a volume conservation hypothesis, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258), 1999.
DOI : 10.1109/ICASSP.1999.757568

D. Béréziat, I. Herlin, and L. Younes, A generalized optical flow constraint and its physical interpretation, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662), pp.487-492, 2000.
DOI : 10.1109/CVPR.2000.854890

D. Béréziat, I. L. Herlin, G. Giraudon, C. Nguyen, and C. Graffigne, Segmentation of echocardiographic images with markov random fields, European Conference on Computer Vision (ECCV), pp.201-206, 1994.

A. Abed, S. Dubuisson, and D. Béréziat, Comparison of statistical and shape-based approaches for non-rigid motion tracking with missing data using a particle filter, Advanced Concepts for Intelligent Vision Systems (ACVIS), pp.185-196, 2006.
URL : https://hal.archives-ouvertes.fr/hal-01351600

A. Abed, S. Dubuisson, and D. Béréziat, Energetic particle filter for online multiple target tracking, International Conference on Image Processing, pp.493-496, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00635937

A. Abed, S. Dubuisson, and D. Béréziat, Energy minimization approach for online data association with missing data, International Conference on Computer Vision Theory and Application (VISAAP), pp.371-378, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00636018

A. Abed, S. Dubuisson, and D. Béréziat, ENMIM : Energetic normalized mutual information model for online multiple object tracking with unlearned motions, Advanced Concepts for Intelligent Vision Systems (ACVIS), pp.955-967, 2007.
URL : https://hal.archives-ouvertes.fr/inria-00635919

J. Grazzini, D. Béréziat, and I. Herlin, Analysis of cloudy structures evolution on meteorological satellite acquisitions, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), pp.7-10, 2001.
DOI : 10.1109/ICIP.2001.958230

E. Huot, I. Herlin, and D. Béréziat, Segmentation of temporal effects on phasimetric SAR images, Proceedings. Fourteenth International Conference on Pattern Recognition (Cat. No.98EX170), pp.1390-1392, 1998.
DOI : 10.1109/ICPR.1998.711962
URL : https://hal.archives-ouvertes.fr/inria-00532717

W. Rekik, D. Béréziat, and S. Dubuisson, MAPVIS: A Map-Projection Based Tool for Visualizing Scalar and Vectorial Information Lying on Spheroidal Surfaces, Ninth International Conference on Information Visualisation (IV'05), pp.636-641, 2005.
DOI : 10.1109/IV.2005.76
URL : https://hal.archives-ouvertes.fr/hal-01416431

W. Rekik, D. Béréziat, and S. Dubuisson, Image Processing in Spheric-Shaped Data Using a Geographical Transformation: Application to Bio-Cellular Imaging, 3rd IEEE International Symposium on Biomedical Imaging: Macro to Nano, 2006., pp.1256-1259, 2006.
DOI : 10.1109/ISBI.2006.1625153
URL : https://hal.archives-ouvertes.fr/hal-01351607

W. Rekik, D. Béréziat, and S. Dubuisson, Optical flow computation and visualization in spherical context. Application on 3D+t bio-cellular sequences, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp.1645-1648, 2006.
DOI : 10.1109/IEMBS.2006.260503
URL : https://hal.archives-ouvertes.fr/hal-01351599

W. Rekik, D. Béréziat, and S. Dubuisson, 3D+t Reconstruction in the Context of Locally Spheric Shaped Data Observation, International Conference on Computer Analysis of Images and Patterns (CAIP), pp.482-489, 2007.
DOI : 10.1007/978-3-540-74272-2_60
URL : https://hal.archives-ouvertes.fr/inria-00635911

W. Rekik, D. Béréziat, S. Dubuisson, and N. Puff, Tracking of non-rigid structures evolving on 3D surfaces by compensating perspective view deformations, IASTED International Conference on Signal and Image ProcessingUSA), 2004.

R. Teina, D. Béréziat, and B. Stoll, A spatial Poisson Point Process to classify coconut fields on Ikonos pansharpened images, Multispectral, Hyperspectral, and Ultraspectral Remote Sensing Technology, Techniques, and Applications II, p.71491, 2008.
DOI : 10.1117/12.806422
URL : https://hal.archives-ouvertes.fr/inria-00582390

R. Teina, D. Béréziat, B. Stoll, and S. Chabrier, Toward a Global Tuamotu Archipelago Coconut Trees Sensing Using High Resolution Optical Data, IGARSS 2008, 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008.
DOI : 10.1109/IGARSS.2008.4779114
URL : https://hal.archives-ouvertes.fr/inria-00582826

R. Teina, D. Béréziat, B. Stoll, and S. Chabrier, A comparative study of several supervised classifiers for coconut tree field's type mapping on 80 cm rgb pansharpened Ikonos images, Image Processing : Machine Vision Applications II. Proceedings of SPIE, p.72510, 2009.

R. Black and M. , Recursive non-linear estimation of discontinuous flow fields, European Conference on Computer Vision, pp.238-245, 1994.
DOI : 10.1007/3-540-57956-7_15

M. Gennert and S. Negahdaripour, Relaxing the brightness constancy assumption in optical flow, 1987.

I. Herlin, I. Cohen, and D. Béréziat, Wind estimation by image processing for air pollution modelling, SAMS, vol.32, pp.57-66, 1998.

B. K. Horn and B. G. Schunck, Determining optical flow, Artificial Intelligence, vol.17, issue.1-3, pp.185-203, 1981.
DOI : 10.1016/0004-3702(81)90024-2

P. Kornprobst, R. Deriche, and A. Gilles, Non linear operators in image restoration, pp.97-325, 1997.

H. Laurent, Wind extraction from multiple meteosat channels, Proc. Workshop on Wind Extraction from Operational Meteorological Satellite Data, 1991.

H. Laurent, Wind Extraction from Meteosat Water Vapor Channel Image Data, Journal of Applied Meteorology, vol.32, issue.6, pp.1124-1133, 1993.
DOI : 10.1175/1520-0450(1993)032<1124:WEFMWV>2.0.CO;2

H. H. Nagel, Displacement vectors derived from second-order intensity variations in image sequences, Computer Vision, Graphics, and Image Processing, vol.21, issue.1, pp.85-117, 1983.
DOI : 10.1016/S0734-189X(83)80030-9

J. M. Odobez and P. Bouthemy, Robust Multiresolution Estimation of Parametric Motion Models, Journal of Visual Communication and Image Representation, vol.6, issue.4, pp.222-238, 1995.
DOI : 10.1006/jvci.1995.1029

B. Schunck, The motion constraint equation for optical flow, Proc. 7th ICPR, pp.20-22, 1984.

M. Tistarelli, Multiple constraints to compute optical flow, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.18, issue.12, pp.1243-1250, 1996.
DOI : 10.1109/34.546260

C. S. Velden, C. M. Hayden, S. Nieman, W. P. Wenzel, S. Wanzong et al., Upper-Tropospheric Winds Derived from Geostationary Satellite Water Vapor Observations, Bulletin of the American Meteorological Society, vol.78, issue.2, pp.173-195, 1997.
DOI : 10.1175/1520-0477(1997)078<0173:UTWDFG>2.0.CO;2

A. Verri, F. Girosi, and V. Torre, Differential techniques for optical flow, Journal of the Optical Society of America A, vol.7, issue.5, pp.912-922, 1990.
DOI : 10.1364/JOSAA.7.000912

W. Rekik, D. Béréziat, and S. Dubuisson, Optical flow computation and visualization in spherical context. Application on 3D+t bio-cellular sequences, 2006 International Conference of the IEEE Engineering in Medicine and Biology Society, pp.1645-1648, 1984.
DOI : 10.1109/IEMBS.2006.260503
URL : https://hal.archives-ouvertes.fr/hal-01351599

A. Gueziec, P. Kazanzides, B. Williamson, and R. Taylor, Anatomy-based registration of CT-scan and intraoperative X-ray images for guiding a surgical robot, IEEE Transactions on Medical Imaging, vol.17, issue.5, pp.715-728, 1998.
DOI : 10.1109/42.736023

B. K. Horn and B. G. Schunk, Determining optical flow, Artificial Intelligence, vol.17, issue.1-3, pp.185-203, 1981.
DOI : 10.1016/0004-3702(81)90024-2

L. Huei-yung, Computer vision techniques for complete 3D model reconstruction, 2002.

S. Lee, G. Wolberg, and S. Shin, Scattered data interpolation with multilevel B-splines, IEEE Transactions on Visualization and Computer Graphics, vol.3, issue.3, pp.228-244, 1997.
DOI : 10.1109/2945.620490

L. Lemieux, R. Jagoe, D. Fish, N. Kitchen, and D. , 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

F. Natterer, The mathematics of computerized tomography, 1986.

W. Rekik, D. Béréziat, and S. Dubuisson, MAPVIS: A Map-Projection Based Tool for Visualizing Scalar and Vectorial Information Lying on Spheroidal Surfaces, Ninth International Conference on Information Visualisation (IV'05), 2005.
DOI : 10.1109/IV.2005.76
URL : https://hal.archives-ouvertes.fr/hal-01416431

G. Staneva, M. Angelova, and K. Koumanov, Phospholipase A2 promotes raft budding and fission from giant liposomes, Chemistry and Physics of Lipids, vol.129, issue.1, pp.53-62, 2004.
DOI : 10.1016/j.chemphyslip.2003.11.005

P. Thévenaz, T. Blu, and M. Unser, Image interpolation and resampling, Handbook of Medical Imaging, pp.393-420, 2000.

A. Tikhonov, Regularization of incorrectly posed problems, Sov. Math. Dokl, vol.4, pp.1624-1627, 1963.

J. Weickert and C. Schnörr, Variational optic flow computation with a spatio-temporal smoothness constraint, Journal of Mathematical Imaging and Vision, vol.14, issue.3, pp.245-255, 2001.
DOI : 10.1023/A:1011286029287

R. Glowinski, Numerical Methods for Nonlinear Variational Problems. Series in computational physics, 1984.

B. K. Horn and B. G. Schunck, Determining optical flow, Artificial Intelligence, vol.17, issue.1-3, pp.185-203, 1981.
DOI : 10.1016/0004-3702(81)90024-2

L. Huei-yung, Computer vision techniques for complete 3D model reconstruction, 2002.

L. Lemieux, R. Jagoe, D. R. Fish, N. D. Kitchen, and D. G. 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

B. Lucas and T. Kanade, An iterative image registration technique with an application to stereo vision, Proceedings of the 7th International Joint Conference on Artificial Intelligence, pp.674-697, 1981.

J. B. Maintz and M. A. Viergever, A survey of medical image registration, Medical Image Analysis, vol.2, issue.1, 1998.
DOI : 10.1016/S1361-8415(01)80026-8

F. Natterer, The mathematics of computerized tomography, 1986.

W. Rekik, Fusion de données temporelles, ou 2D+t, et spatiales, ou 3D, pour la reconstruction de scènes 3D+t et traitement d'images sphériques. Applications à la biologie cellulaire, 2007.

W. Rekik, D. Béréziat, and S. Dubuisson, 3D+t Reconstruction in the Context of Locally Spheric Shaped Data Observation, 12th International Conference on Computer Analysis of Images and Patterns, 2007.
DOI : 10.1007/978-3-540-74272-2_60
URL : https://hal.archives-ouvertes.fr/inria-00635911

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

F. Natterer, The mathematics of computerized tomography, 1986.

L. Huei-yung, Computer vision techniques for complete 3D model reconstruction, 2002.

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

R. Glowinski, Numerical Methods for Nonlinear Variational Problems, 1984.

W. Rekik, D. Béréziat, and S. Dubuisson, MAPVIS: A Map-Projection Based Tool for Visualizing Scalar and Vectorial Information Lying on Spheroidal Surfaces, Ninth International Conference on Information Visualisation (IV'05), 2005.
DOI : 10.1109/IV.2005.76
URL : https://hal.archives-ouvertes.fr/hal-01416431

J. Weickert and C. Schnrr, Variational optic flow computation with a spatio-temporal smoothness constraint, Journal of Mathematical Imaging and Vision, vol.14, issue.3, pp.245-255, 2001.
DOI : 10.1023/A:1011286029287

D. Béréziat and I. Herlin, Solving ill-posed image processing problems using data assimilation . Numerical Algorithms, to appear, 2010.

D. Béréziat, I. Herlin, L. Alvarez, J. Weickert, and J. Sánchez, Using model of dynamics for large displacement estimation on noisy acquisitions Reliable estimation of dense optical flow fields with large displacements, International Journal of Computer Vision, vol.39, issue.1, pp.41-56, 2000.

A. Apte, C. K. Jones, A. M. Stuart, and J. Voss, Data assimilation: Mathematical and statistical perspectives, International Journal for Numerical Methods in Fluids, vol.53, issue.8, pp.1033-1046, 2008.
DOI : 10.1002/fld.1698

T. Brox, A. Bruhn, N. Papenberg, and J. Weickert, High Accuracy Optical Flow Estimation Based on a Theory for Warping, Proceedings of European Conference on Computer Vision, pp.25-36, 2004.
DOI : 10.1007/978-3-540-24673-2_3

I. Herlin, F. Dimet, E. Huot, and J. Berroir, Coupling models and data: which possibilities for remotely-sensed images?, Progress and Challenge, pp.365-383, 2004.
URL : https://hal.archives-ouvertes.fr/inria-00527201

B. K. Horn and B. G. Schunk, Determining optical flow, Artificial Intelligence, vol.17, issue.1-3, pp.185-203, 1981.
DOI : 10.1016/0004-3702(81)90024-2

E. Huot, I. Herlin, and G. Korotaev, Assimilation of SST Satellite Images for Estimation of Ocean Circulation Velocity, IGARSS 2008, 2008 IEEE International Geoscience and Remote Sensing Symposium, 2008.
DOI : 10.1109/IGARSS.2008.4779127
URL : https://hal.archives-ouvertes.fr/inria-00583029

F. Le-dimet and O. Talagrand, Variational algorithms for analysis and assimilation of meteorological observations: theoretical aspects, Tellus A: Dynamic Meteorology and Oceanography, vol.109, issue.2, pp.97-110, 1986.
DOI : 10.3402/tellusa.v38i2.11706

F. Dimet, I. M. Navon, and D. N. Daescu, Second-Order Information in Data Assimilation*, Monthly Weather Review, vol.130, issue.3, pp.629-648, 2002.
DOI : 10.1175/1520-0493(2002)130<0629:SOIIDA>2.0.CO;2
URL : https://hal.archives-ouvertes.fr/hal-00787272

D. Mumford and J. Shah, Optimal approximations by piecewise smooth functions and associated variational problems, Communications on Pure and Applied Mathematics, vol.3, issue.5, pp.577-685, 1989.
DOI : 10.1002/cpa.3160420503

H. Nagel, Displacement vectors derived from second-order intensity variations in image sequences, Computer Vision, Graphics, and Image Processing, pp.85-117, 1983.

J. Odobez and P. Bouthemy, Direct incremental model-based image motion segmentation for video analysis, Signal Processing, vol.66, issue.2, pp.143-155, 1998.
DOI : 10.1016/S0165-1684(98)00003-6

D. S. Oliver, Calculation of the inverse of the covariance, Mathematical Geology, vol.30, issue.7, pp.911-933, 1998.
DOI : 10.1023/A:1021734811230

N. Papadakis, T. Corpetti, and . Mémin, Dynamically consistent optical flow estimation, 2007 IEEE 11th International Conference on Computer Vision, 2007.
DOI : 10.1109/ICCV.2007.4408889
URL : https://hal.archives-ouvertes.fr/hal-00596200

N. Papadakis, P. Héas, and . Mémin, Image Assimilation for Motion Estimation of Atmospheric Layers with Shallow-Water Model, Proceedings of Asian Conference on Computer Vision, pp.864-874, 2007.
DOI : 10.1007/978-3-540-76386-4_82
URL : https://hal.archives-ouvertes.fr/hal-00596194

N. Papadakis and . Mémin, Variational optimal control technique for the tracking of deformable objects, 2007 IEEE 11th International Conference on Computer Vision, 2007.
DOI : 10.1109/ICCV.2007.4408944
URL : https://hal.archives-ouvertes.fr/hal-00596203

N. Papadakis, E. Mémin, and F. Cao, A Variational Approach for Object Contour Tracking, Proceedings of ICCV'05 Workshop on Variational, Geometric and Level Set Methods in Computer Vision, 2005.
DOI : 10.1007/11567646_22
URL : https://hal.archives-ouvertes.fr/hal-00655852

P. Perona and J. Malik, Scale-space and edge detection using anisotropic diffusion, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.12, issue.7, pp.629-639, 1990.
DOI : 10.1109/34.56205

M. Proesmans, L. Van-gool, E. Pauwels, and A. Oosterlinck, Determination of optical flow and its discontinuities using non-linear diffusion, Proceedings of European Conference on Computer Vision, pp.295-304, 1994.
DOI : 10.1007/BFb0028362

J. A. Sethian, Level Set Methods, 1996.

A. Tarantola, Inverse Problem Theory and Methods for Model Parameter Estimation, Society for Industrial and Applied Mathematics, 2005.
DOI : 10.1137/1.9780898717921

A. N. Tikhonov, Regularization of incorrectly posed problems, Sov. Math. Dokl, vol.4, pp.1624-1627, 1963.

J. G. Verwer and B. Sportisse, A note on operator splitting in a stiff linear case, 1998.
URL : https://hal.archives-ouvertes.fr/inria-00532710

J. Weickert, Anisotropic diffusion in image processing. ECMI Series, 1998.

J. Weickert, Applications of nonlinear diffusion in image processing and computer vision, Acta Math. Univ. Comenianae. Proceeding of Algoritmy, pp.33-50, 2000.

A. P. Witkin, SCALE-SPACE FILTERING, Proc. 8th Int. Joint Conf. Art. Intell, pp.1019-1022, 1983.
DOI : 10.1016/B978-0-08-051581-6.50036-2

W. Ames, Numerical Methods for Partial Differential Equations, 1977.

D. Béréziat and I. Herlin, Solving ill-posed image processing problems using data assimilation. Numerical Algorithms, to appear, 2010.

T. Brox, A. Bruhn, N. Papenberg, and J. Weickert, High Accuracy Optical Flow Estimation Based on a Theory for Warping, ECCV, pp.25-36, 2004.
DOI : 10.1007/978-3-540-24673-2_3

B. Horn and B. Schunk, Determining optical flow, Artificial Intelligence, vol.17, issue.1-3, pp.185-203, 1981.
DOI : 10.1016/0004-3702(81)90024-2

E. Huot, I. Herlin, and G. Korotaev, Assimilation of SST Satellite Images for Estimation of Ocean Circulation Velocity, IGARSS 2008, 2008 IEEE International Geoscience and Remote Sensing Symposium, 2002.
DOI : 10.1109/IGARSS.2008.4779127
URL : https://hal.archives-ouvertes.fr/inria-00583029

T. Isambert, J. Berroir, and I. Herlin, A multiscale vector spline method for estimating the fluids motion on satellite images, 2002.
URL : https://hal.archives-ouvertes.fr/inria-00264727

J. Odobez and P. Bouthemy, Direct incremental model-based image motion segmentation for video analysis, Signal Processing, vol.66, issue.2, pp.143-155, 1998.
DOI : 10.1016/S0165-1684(98)00003-6

D. Oliver, Calculation of the inverse of the covariance, Mathematical Geology, vol.30, issue.7, pp.911-933, 1998.
DOI : 10.1023/A:1021734811230

N. Papadakis, T. Corpetti, and E. Mémin, Dynamically consistent optical flow estimation, 2007 IEEE 11th International Conference on Computer Vision, 2001.
DOI : 10.1109/ICCV.2007.4408889
URL : https://hal.archives-ouvertes.fr/hal-00596200

N. Papadakis, P. Héas, and E. Mémin, Image Assimilation for Motion Estimation of Atmospheric Layers with Shallow-Water Model, ACCV, pp.864-874, 2001.
DOI : 10.1007/978-3-540-76386-4_82
URL : https://hal.archives-ouvertes.fr/hal-00596194

M. Proesmans, L. Van-gool, E. Pauwels, and A. Oosterlinck, Determination of optical flow and its discontinuities using non-linear diffusion, ECCV, pp.295-304, 1994.
DOI : 10.1007/BFb0028362

A. N. Tikhonov, Regularization of incorrectly posed problems, Sov. Math. Dokl, vol.4, issue.1 2, pp.1624-1627, 1963.

E. and V. Hólm, Lectures notes on assimilation algorithms European Centre for Medium-Range Weather Forecasts Reading, 2003.

R. Wildes and M. Amabile, Physically based fluid flow recovery from image sequences, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp.969-975, 1997.
DOI : 10.1109/CVPR.1997.609445

R. Teina, D. Béréziat, B. Stoll, and S. Chabrier, A comparative study of several supervised classifiers for coconut tree field's type mapping on 80 cm rgb pansharpened Ikonos images

R. Teina, D. Béréziat, B. Stoll, and S. Chabrier, Toward a Global Tuamotu Archipelago Coconut Trees Sensing Using High Resolution Optical Data, IGARSS 2008, 2008 IEEE International Geoscience and Remote Sensing Symposium, p.8, 2008.
DOI : 10.1109/IGARSS.2008.4779114
URL : https://hal.archives-ouvertes.fr/inria-00582826

B. D. Ripley, Modelling spatial patterns, Journal of the Royal Statistical Society, Series B, vol.39, pp.172-212, 1977.

P. J. Diggle, [Statistical Analysis of Spatial Point Patterns ], 1983.

P. J. Clark and F. C. Evans, Distance to Nearest Neighbor as a Measure of Spatial Relationships in Populations, Ecology, vol.35, issue.4, p.35, 1954.
DOI : 10.2307/1931034

J. A. Ludwig and J. F. Reynolds, [Statistical ecology: a primer on methods & computing, 1988.

K. Jayaraman, A statistical manual for forestry research. Forestry Statistics and Data Collection - DCA/MISC/01, 2000.

R. B. Johnson and W. J. Zimmer, A More Powerful Test for Dispersion Using Distance Measurements, Ecology, vol.66, issue.5, pp.1669-1675, 1985.
DOI : 10.2307/1938029

P. Diggle, A Kernel Method for Smoothing Point Process Data, Applied Statistics, vol.34, issue.2, pp.138-147, 1985.
DOI : 10.2307/2347366

A. J. Baddeley, Spatial sampling and censoring, " in [Stochastic Geometry: Likelihood and Computation, pp.37-78, 1998.

D. Stoyan and H. Stoyan, Random shapes and point fields, methods of geometrical statistics, 1994.

D. Stoyan and H. Stoyan, Estimating Pair Correlation Functions of Planar Cluster Processes, Biometrical Journal, vol.6, issue.3, pp.259-271, 1996.
DOI : 10.1002/bimj.4710380302

G. Chantry and Y. W. Cabannes, [Le cocotier: production et mise en oeuvre dans l'habitat], Ministère de la Coopération, pp.2-11, 1983.

A. Baddeley, R. Turner, J. Moller, and M. Hazelton, Residual analysis for spatial point processes (with discussion), Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.151, issue.6, pp.617-666, 2005.
DOI : 10.1029/2003JB002879

H. Anys, H. Bannari, D. C. He, and D. Morin, Cartographie des zones urbaines a l'aide des images aeroportees MEIS-II, International Journal of Remote Sensing, vol.19, issue.5, pp.883-894, 1998.
DOI : 10.1080/014311698215775

A. Puissant, J. Hirsch, and C. Weber, The utility of texture analysis to improve per???pixel classification for high to very high spatial resolution imagery, International Journal of Remote Sensing, vol.5, issue.4, pp.733-745, 2005.
DOI : 10.1016/S0924-2716(98)00027-6

R. M. Haralick, K. Shanmugam, and I. Dinstein, Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, vol.3, issue.6, pp.610-621, 1973.
DOI : 10.1109/TSMC.1973.4309314

R. M. Haralick, Statistical and structural approaches to texture, Proceedings of the IEEE, pp.786-804, 1979.
DOI : 10.1109/PROC.1979.11328

H. Anys, H. Bannari, D. C. He, and D. Morin, Texture analysis for the mapping of urban areas using airborne MEIS-II images, pp.231-245, 1994.

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, pp.273-297, 1995.
DOI : 10.1007/BF00994018

C. J. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.121-167, 1998.
DOI : 10.1023/A:1009715923555

F. Jacq, ´ Evaluation quantitative et qualitative des peuplements de cocotiers sur tikehau, Service du Développement Rural - Département FOGER, 2006.

J. A. Richards and X. Jia, Remote Sensing Digital Image Analysis: An Introduction, 1999.

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, 2001.
DOI : 10.1145/1961189.1961199

T. Wu, C. Lin, and R. C. Weng, Probability estimates for multiclass classification by pairwise coupling, Journal of Machine Learning Research, vol.5, pp.975-1005, 2004.

L. Wang, P. Gong, and G. S. Biging, Individual Tree-Crown Delineation and Treetop Detection in High-Spatial-Resolution Aerial Imagery, Photogrammetric Engineering & Remote Sensing, vol.70, issue.3, pp.351-357, 2004.
DOI : 10.14358/PERS.70.3.351

C. Ricotta and C. Avena, The influence of principal component analysis on the spatial structure of a multispectral dataset, International Journal of Remote Sensing, vol.20, issue.17, pp.3367-3376, 1999.

R. J. Pollock, The automatic Recognition of Individual trees in Aerial Images of Forests Based on a Synthetic Tree Crown Image Model, 1996.

F. A. Gougeon, Automatic individual tree crown delineation using a valley-following algorithm and a rule-based system Forum on Automated Interpretation of High Spatial Resolution Digital Imagery for Forestry, Proceeding of Int, pp.11-23, 1998.

D. S. Culvenor, TIDA: an algorithm for the delineation of tree crowns in high spatial resolution remotely sensed imagery, Computers & Geosciences, vol.28, issue.1, pp.33-44, 2002.
DOI : 10.1016/S0098-3004(00)00110-2

C. Cortes and V. Vapnik, Support-vector networks, Machine Learning, vol.1, issue.3, pp.273-297, 1995.
DOI : 10.1007/BF00994018

C. J. Burges, A tutorial on support vector machines for pattern recognition, Data Mining and Knowledge Discovery, vol.2, issue.2, pp.121-167, 1998.
DOI : 10.1023/A:1009715923555

T. Wu, C. Lin, and R. C. Weng, Probability estimates for multi-class classification by pairwise coupling, Journal of Machine Learning Research, vol.5, pp.975-1005, 2004.

P. Swain and S. Davis, Remote Sensing: The Quantitative Approach, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.3, issue.6, 1978.
DOI : 10.1109/TPAMI.1981.4767177

A. Wacker, The Minimum Distance Approach to Classification, 1971.

T. Kailath, The Divergence and Bhattacharyya Distance Measures in Signal Selection, IEEE Transactions on Communications, vol.15, issue.1
DOI : 10.1109/TCOM.1967.1089532

R. M. Haralick, . Dinstein, and K. Shanmugam, Textural Features for Image Classification, IEEE Transactions on Systems, Man, and Cybernetics, vol.3, issue.6, pp.610-621, 1973.
DOI : 10.1109/TSMC.1973.4309314

R. Haralick, Statistical and structural approaches to texture, Proceedings of the IEEE, vol.67, issue.5, pp.786-804, 1979.
DOI : 10.1109/PROC.1979.11328

H. Anys, H. Bannari, D. He, and D. Morin, Texture analysis for the mapping of urban areas using airborne MEIS-II images, pp.231-245, 1994.

A. Puissant, J. Hirsch, W. , and C. , The utility of texture analysis to improve per???pixel classification for high to very high spatial resolution imagery, International Journal of Remote Sensing, vol.5, issue.4, pp.733-745, 2005.
DOI : 10.1016/S0924-2716(98)00027-6

C. Chang and C. Lin, LIBSVM, ACM Transactions on Intelligent Systems and Technology, vol.2, issue.3, 2001.
DOI : 10.1145/1961189.1961199