.. Constraints-integration, 115 5.4.1 Size constraint, p.115

R. Achanta, A. Shaji, K. Smith, P. Lucchi, S. Fua et al., SLIC Superpixels Compared to State-of-the-Art Superpixel Methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.11, pp.2274-2282, 2012.
DOI : 10.1109/TPAMI.2012.120

F. Achard, Vital forest graphics, UNEP/Earthprint, 2009.

L. Albert, F. Rottensteiner, and C. Heipke, Contextual land use classification: how detailed can the class structure be? " International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B4, pp.11-18, 2016.

G. L. Anderson and J. D. Hanson, Evaluating hand???held radiometer derived vegetation indices for estimating above ground biomass, Geocarto International, vol.54, issue.1, pp.71-78, 1992.
DOI : 10.2307/3898123

P. M. Atkinson, M. E. Cutler, and H. Lewis, Mapping sub-pixel proportional land cover with AVHRR imagery, International Journal of Remote Sensing, vol.18, issue.4, pp.917-935, 1997.
DOI : 10.1080/014311697218836

N. Audebert, B. L. Saux, and S. Lefèvre, Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-scale Deep Networks, Asian Conference on Computer Vision, pp.180-196, 2016.
DOI : 10.1127/1432-8364/2010/0041
URL : https://hal.archives-ouvertes.fr/hal-01360166

M. Baatz, G. Binnig, P. Eschenbacher, A. Melchinger, and M. Sogtrop, Method of iterative segmentation of a digital picture, US Patent, vol.6832, p.2, 2004.

C. Bacour, F. Bréon, and F. Maignan, Normalization of the directional effects in NOAA???AVHRR reflectance measurements for an improved monitoring of vegetation cycles, Remote Sensing of Environment, vol.102, issue.3-4, pp.402-413, 2006.
DOI : 10.1016/j.rse.2006.03.006

A. Bannari, D. Morin, F. Bonn, and A. R. Huete, A review of vegetation indices, Remote Sensing Reviews, vol.43, issue.1-2, pp.95-120, 1995.
DOI : 10.1007/BF00036048

R. Battiti, Using mutual information for selecting features in supervised neural net learning, IEEE Transactions on Neural Networks, vol.5, issue.4, pp.537-550, 1994.
DOI : 10.1109/72.298224

A. Beaudoin, T. Le-toan, S. Goze, E. Nezry, A. Lopes et al., Retrieval of forest biomass from SAR data, International Journal of Remote Sensing, vol.5, issue.14, pp.2777-2796, 1994.
DOI : 10.1109/36.134088

M. Belgiu and L. Dr?-agu¸tagu¸t, Random forest in remote sensing: A review of applications and future directions, ISPRS Journal of Photogrammetry and Remote Sensing, vol.114, pp.24-31, 2016.
DOI : 10.1016/j.isprsjprs.2016.01.011

R. E. Bellman, Adaptive control processes: a guided tour, 2015.
DOI : 10.1515/9781400874668

A. S. Belward and J. O. Skøien, Who launched what, when and why; trends in global land-cover observation capacity from civilian earth observation satellites, ISPRS Journal of Photogrammetry and Remote Sensing, vol.103, pp.115-128, 2015.
DOI : 10.1016/j.isprsjprs.2014.03.009

S. Beucher and C. Lantuéjoul, Use of watersheds in contour detection, 1979.

A. Bosch, A. Zisserman, and X. Munoz, Image Classification using Random Forests and Ferns, 2007 IEEE 11th International Conference on Computer Vision, pp.1-8, 2007.
DOI : 10.1109/ICCV.2007.4409066

B. E. Boser, I. M. Guyon, and V. N. Vapnik, A training algorithm for optimal margin classifiers, Proceedings of the fifth annual workshop on Computational learning theory , COLT '92, pp.144-152, 1992.
DOI : 10.1145/130385.130401

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

G. Bradski and A. Kaehler, Learning OpenCV: Computer vision with the OpenCV library, 2008.

T. Brandtberg, Classifying individual tree species under leaf-off and leaf-on conditions using airborne lidar, ISPRS Journal of Photogrammetry and Remote Sensing, vol.61, issue.5, pp.325-340, 2007.
DOI : 10.1016/j.isprsjprs.2006.10.006

L. Breiman, Random forests, Machine learning 45.1, pp.5-32, 2001.

L. Breiman, J. Friedman, C. J. Stone, and R. A. Olshen, Classification and regression trees, 1984.

G. Mittermeier, J. D. Pilgrim, and A. S. Rodrigues, Global biodiversity conservation priorities " . Science 313, pp.58-61, 2006.

L. Bruzzone and S. B. Serpico, A technique for feature selection in multiclass problems, International Journal of Remote Sensing, vol.21, issue.3, pp.549-563, 2000.
DOI : 10.1080/014311600210740

B. C. Budei, B. St-onge, C. Hopkinson, and F. A. Audet, Identifying the genus or species of individual trees using a three-wavelength airborne lidar system " . Remote Sensing of Environment, pp.34-4257, 2017.

G. Camps-valls and L. Bruzzone, Kernel methods for remote sensing data analysis, 2009.
DOI : 10.1002/9780470748992

S. Cang and H. Yu, Mutual information based input feature selection for classification problems " . Decision Support Systems 54, pp.691-698, 2012.
DOI : 10.1016/j.dss.2012.08.014

M. J. Carlotto, Effect of errors in ground truth on classification accuracy, International Journal of Remote Sensing, vol.46, issue.18, pp.4831-4849, 2009.
DOI : 10.1016/j.atmosenv.2005.03.024

T. F. Chan and L. A. Vese, Active contours without edges, IEEE Transactions on image processing 10.2, pp.266-277, 2001.
DOI : 10.1109/83.902291
URL : http://www.math.ucla.edu/~lvese/PAPERS/IEEEIP2001.pdf

Y. Chang, K. Chen, B. Huang, W. Chang, J. A. Benediktsson et al., A Parallel Simulated Annealing Approach to Band Selection for High-Dimensional Remote Sensing Images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.4, issue.3, 2011.
DOI : 10.1109/JSTARS.2011.2160048

J. Chen, S. Paris, and F. Durand, Real-time edge-aware image processing with the bilateral grid, ACM Transactions on Graphics (TOG), vol.263, p.103, 2007.

A. Cheriyadat and L. M. Bruce, Why principal component analysis is not an appropriate feature extraction method for hyperspectral data, IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477), pp.3420-3422, 2003.
DOI : 10.1109/IGARSS.2003.1294808

M. L. Clark, D. A. Roberts, and D. B. Clark, Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales " . Remote sensing of environment 96, pp.375-398, 2005.

J. Cohen, A coefficient of agreement for nominal scales " . Educational and psychological measurement 20, pp.37-46, 1960.

D. Comaniciu and P. Meer, Mean shift: a robust approach toward feature space analysis, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.5, pp.603-619, 2002.
DOI : 10.1109/34.1000236

M. Dalponte, L. Bruzzone, and D. Gianelle, Tree species classification in the Southern Alps based on the fusion of very high geometrical resolution multispectral/hyperspectral images and LiDAR data, Remote Sensing of Environment, vol.123, pp.258-270, 2012.
DOI : 10.1016/j.rse.2012.03.013

M. Dalponte, H. O. Orka, T. Gobakken, D. Gianelle, and E. Naesset, Tree Species Classification in Boreal Forests With Hyperspectral Data, IEEE Transactions on Geoscience and Remote Sensing, vol.51, issue.5, pp.2632-2645, 2013.
DOI : 10.1109/TGRS.2012.2216272

M. Dalponte, H. O. Ørka, L. T. Ene, T. Gobakken, and E. Naesset, Tree crown delineation and tree species classification in boreal forests using hyperspectral and ALS data " . Remote Sensing of Environment 140, pp.306-317, 2014.

M. Dalponte, L. T. Ene, C. Witte, M. Marconcini, T. Gobakken et al., Semi-supervised SVM for individual tree crown species classification, ISPRS Journal of Photogrammetry and Remote Sensing, vol.110, issue.16, pp.77-87, 2015.
DOI : 10.1016/j.isprsjprs.2015.10.010

D. Backer, S. , P. Kempeneers, W. Debruyn, and P. Scheunders, A band selection technique for spectral classification, IEEE Geoscience and Remote Sensing Letters 2.3, pp.319-323, 2005.

J. Dean, G. S. Corrado, R. Monga, K. Chen, M. Devin et al., Large scale distributed deep networks, Advances in Neural Information Processing Systems, pp.1223-1231, 2012.

C. Dechesne, C. Mallet, A. Le-bris, V. Gouet-brunet, and A. Hervieu, Forest stand segmentation using airborne lidar data and very high resolution multispectral imagery " . International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.41-44, 2016.
DOI : 10.5194/isprs-archives-xli-b3-207-2016
URL : https://doi.org/10.5194/isprs-archives-xli-b3-207-2016

C. Dechesne, C. Mallet, A. L. Bris, and V. Gouet-brunet, Semantic segmentation of forest stands of pure species combining airborne lidar data and very high resolution multispectral imagery, ISPRS Journal of Photogrammetry and Remote Sensing, vol.126, issue.22, pp.129-145, 2017.
DOI : 10.1016/j.isprsjprs.2017.02.011

J. Demantké, C. Mallet, N. David, and B. Vallet, Dimensionality based scale selection in 3D lidar point clouds " . The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 38, p.12, 2011.

H. B. Demuth, M. H. Beale, O. De-jess, and M. T. Hagan, Neural network design, 2014.

H. Derin and H. Elliott, Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.9, issue.1, pp.39-55, 1987.
DOI : 10.1109/TPAMI.1987.4767871

R. Díaz-uriarte and S. A. De-andres, Gene selection and classification of microarray data using random forest, BMC Bioinformatics, vol.7, issue.1, p.3, 2006.
DOI : 10.1186/1471-2105-7-3

O. Diedershagen, B. Koch, and H. Weinacker, Automatic segmentation and characterisation of forest stand parameters using airborne lidar data, multispectral and fogis data " . International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.36, issue.20, pp.208-212, 2004.

P. A. Estévez, M. Tesmer, C. A. Perez, and J. M. Zurada, Normalized Mutual Information Feature Selection, IEEE Transactions on Neural Networks, vol.20, issue.2, pp.189-201, 2009.
DOI : 10.1109/TNN.2008.2005601

L. Eysn, M. Hollaus, K. Schadauer, and N. Pfeifer, Forest Delineation Based on Airborne LIDAR Data, Remote Sensing, vol.72, issue.3, pp.762-783, 2012.
DOI : 10.14358/PERS.72.12.1369
URL : http://www.mdpi.com/2072-4292/4/3/762/pdf

. Woodall, Forest carbon storage: ecology, management, and policy, Frontiers in Ecology and the Environment 8.5, pp.245-252, 2010.

M. Fauvel, C. Dechesne, A. Zullo, and F. Ferraty, Fast Forward Feature Selection of Hyperspectral Images for Classification With Gaussian Mixture Models, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.8, issue.6, pp.2824-2831, 2015.
DOI : 10.1109/JSTARS.2015.2441771

P. F. Felzenszwalb and D. P. Huttenlocher, Efficient Graph-Based Image Segmentation, International Journal of Computer Vision, vol.59, issue.2, pp.167-181, 2004.
DOI : 10.1023/B:VISI.0000022288.19776.77

M. Fernández-delgado, E. Cernadas, S. Barro, and D. Amorim, Do we need hundreds of classifiers to solve real world classification problems, J. Mach. Learn. Res, vol.15, issue.1, pp.3133-3181, 2014.

A. Ferraz, M. Mallet, and N. Chehata, Large-scale road detection in forested mountainous areas using airborne topographic lidar data, ISPRS Journal of Photogrammetry and Remote Sensing, vol.112, pp.23-36, 2016.
DOI : 10.1016/j.isprsjprs.2015.12.002

A. Ferraz, S. Saatchi, C. Mallet, and V. Meyer, Lidar detection of individual tree size in tropical forests, Remote Sensing of Environment, vol.183, pp.318-333, 2016.
DOI : 10.1016/j.rse.2016.05.028

S. Filin and N. Pfeifer, Neighborhood Systems for Airborne Laser Data, Photogrammetric Engineering & Remote Sensing, vol.71, issue.6, pp.743-755, 2005.
DOI : 10.14358/PERS.71.6.743

R. A. Fisher, THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS, Annals of Eugenics, vol.59, issue.2, pp.179-188, 1936.
DOI : 10.1111/j.1469-1809.1936.tb02137.x

G. M. Foody, Status of land cover classification accuracy assessment " . Remote Sensing of Environment 80, pp.185-201, 2002.

S. E. Franklin, R. J. Hall, L. M. Moskal, A. J. Maudie, and M. B. Lavigne, Incorporating texture into classification of forest species composition from airborne multispectral images, International Journal of Remote Sensing, vol.21, issue.1, pp.61-79, 2000.
DOI : 10.1080/014311600210993

K. Fukunaga and L. Hostetler, The estimation of the gradient of a density function, with applications in pattern recognition, IEEE Transactions on Information Theory, vol.21, issue.1, pp.32-40, 1975.
DOI : 10.1109/TIT.1975.1055330

P. Geurts, D. Ernst, and L. Wehenkel, Extremely randomized trees, Machine Learning, vol.63, issue.1, pp.3-42, 2006.
DOI : 10.1007/s10994-006-6226-1
URL : https://hal.archives-ouvertes.fr/hal-00341932

A. Ghosh, N. R. Pal, and S. K. , Image segmentation using a neural network, Biological Cybernetics, vol.8, issue.2, pp.151-158, 1991.
DOI : 10.1007/978-3-642-88163-3

P. O. Gislason, J. A. Benediktsson, and J. R. Sveinsson, Random Forests for land cover classification, Pattern Recognition Letters, vol.27, issue.4, pp.294-300, 2006.
DOI : 10.1016/j.patrec.2005.08.011

D. E. Goldberg, Genetic algorithms in search, optimization, and machine learning, 1989.

P. Gong and P. J. Howarth, Performance analyses of probabilistic relaxation methods for landcover classification " . Remote Sensing of Environment 30, pp.33-42, 1989.

A. Gressin, C. Mallet, N. Vincent, and N. Paparoditis, Updating land cover databases using a single very high resolution satellite image, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.3, issue.2, pp.13-18, 2013.
DOI : 10.5194/isprsannals-II-3-W2-13-2013
URL : https://hal.archives-ouvertes.fr/hal-00920957

L. Guigues, J. Cocquerez, and H. Le-men, Scale-sets image analysis, pp.289-317, 2006.
DOI : 10.1109/icip.2003.1246612
URL : https://hal.archives-ouvertes.fr/hal-00705364

B. Guo, R. I. Damper, S. R. Gunn, and J. D. Nelson, A fast separability-based featureselection method for high-dimensional remotely sensed image classification, Pattern Recognition, vol.415, pp.1653-1662, 2008.

F. R. Hansen and H. Elliott, Image segmentation using simple Markov field models " . Computer Graphics and Image Processing 20, pp.101-132, 1982.
DOI : 10.1016/0146-664x(82)90130-7

S. J. Stehman, T. R. Goetz, A. Loveland, A. Kommareddy, L. Egorov et al., High-resolution global maps of 21st-century forest cover change " . science 342, pp.850-853, 2013.

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
URL : http://www.cis.rit.edu/~cnspci/references/dip/segmentation/haralick1973.pdf

J. Heinzel and B. Koch, Investigating multiple data sources for tree species classification in temperate forest and use for single tree delineation, International Journal of Applied Earth Observation and Geoinformation, vol.18, pp.101-110, 2012.
DOI : 10.1016/j.jag.2012.01.025

G. Hepner, T. S. Logan, N. Ritter, and N. Bryant, Artificial neural network classification using a minimal training set-Comparison to conventional supervised classification, Photogrammetric Engineering and Remote Sensing, vol.564, pp.469-473, 1990.

A. Hernando, D. Tiede, F. Albrecht, and S. Lang, Spatial and thematic assessment of object-based forest stand delineation using an OFA-matrix, International Journal of Applied Earth Observation and Geoinformation, vol.19, issue.18, pp.214-225, 2012.
DOI : 10.1016/j.jag.2012.05.007

M. Herold, M. E. Gardner, and D. A. Roberts, Spectral resolution requirements for mapping urban areas, IEEE Transactions on Geoscience and Remote Sensing, vol.41, issue.9, pp.1907-1919, 2003.
DOI : 10.1109/TGRS.2003.815238

J. Holmgren, Prediction of tree height, basal area and stem volume in forest stands using airborne laser scanning, Scandinavian Journal of Forest Research, vol.68, issue.6, pp.543-553, 2004.
DOI : 10.1016/0034-4257(95)00224-3

A. Hovi, L. Korhonen, J. Vauhkonen, and I. Korpela, LiDAR waveform features for tree species classification and their sensitivity to tree-and acquisition related parameters " . Remote Sensing of Environment 173, pp.224-237, 2016.

G. Hughes, On the mean accuracy of statistical pattern recognizers, IEEE Transactions on Information Theory, vol.14, issue.1, pp.55-63, 1968.
DOI : 10.1109/TIT.1968.1054102

J. Hyyppa, O. Kelle, M. Lehikoinen, and M. Inkinen, A segmentation-based method to retrieve stem volume estimates from 3-D tree height models produced by laser scanners, IEEE Transactions on Geoscience and Remote Sensing, vol.39, issue.5, pp.969-975, 2001.
DOI : 10.1109/36.921414

P. Indyk and R. Motwani, Approximate nearest neighbors, Proceedings of the thirtieth annual ACM symposium on Theory of computing , STOC '98, pp.604-613, 1998.
DOI : 10.1145/276698.276876

P. Jaccard, THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1, New Phytologist, vol.11, issue.2, pp.37-50, 1912.
DOI : 10.1111/j.1469-8137.1912.tb05611.x

M. K. Jakubowski, Q. Guo, and M. Kelly, Tradeoffs between lidar pulse density and forest measurement accuracy, Remote Sensing of Environment, vol.130, pp.245-253, 2013.
DOI : 10.1016/j.rse.2012.11.024

G. H. John, Enhancements to the data mining process, 1997.

I. Jolliffe, Principal component analysis " . In: International Encyclopedia of Statistical Science, pp.1094-1096, 2011.

C. F. Jordan, Derivation of Leaf-Area Index from Quality of Light on the Forest Floor, Ecology, vol.50, issue.4, pp.663-666, 1969.
DOI : 10.2307/1936256

C. Jutten and J. Herault, Blind separation of sources, part I: An adaptive algorithm based on neuromimetic architecture, Signal Processing, vol.24, issue.1, pp.1-10, 1991.
DOI : 10.1016/0165-1684(91)90079-X

F. Hirschmugl, E. Morsdorf, . Naesset, J. Pitkänen, S. Popescu et al., An international comparison of individual tree detection and extraction using airborne laser scanning, pp.950-974, 2012.

S. Kaasalainen, T. Lindroos, and J. Hyyppa, Toward Hyperspectral Lidar: Measurement of Spectral Backscatter Intensity With a Supercontinuum Laser Source, IEEE Geoscience and Remote Sensing Letters 4.2, pp.211-215, 2007.
DOI : 10.1109/LGRS.2006.888848

K. Kandare, M. Dalponte, D. Gianelle, and J. C. Chan, A new procedure for identifying single trees in understory layer using discrete LiDAR data, 2014 IEEE Geoscience and Remote Sensing Symposium, pp.1357-1360, 2014.
DOI : 10.1109/IGARSS.2014.6946686

A. Kangas and M. Maltamo, Forest inventory: methodology and applications, 2006.
DOI : 10.1007/1-4020-4381-3

R. J. Keenan, G. A. Reams, F. Achard, J. V. De-freitas, A. Grainger et al., Dynamics of global forest area: Results from the FAO Global Forest Resources Assessment 2015, Forest Ecology and Management, vol.352, pp.9-20, 2015.
DOI : 10.1016/j.foreco.2015.06.014

A. Khosravipour, A. K. Skidmore, M. Isenburg, T. Wang, and Y. A. Hussin, Generating pitfree canopy height models from airborne lidar, Photogrammetric Engineering & Remote Sensing, vol.809, pp.863-872, 2014.
DOI : 10.14358/pers.80.9.863

M. Kim, M. Madden, and T. A. Warner, Forest Type Mapping using Object-specific Texture Measures from Multispectral Ikonos Imagery, Photogrammetric Engineering & Remote Sensing, vol.75, issue.7, pp.819-829, 2009.
DOI : 10.14358/PERS.75.7.819

S. Kim, T. Hinckley, and D. Briggs, Classifying individual tree genera using stepwise cluster analysis based on height and intensity metrics derived from airborne laser scanner data " . Remote sensing of environment 115, pp.3329-3342, 2011.
DOI : 10.1016/j.rse.2011.07.016

A. Kirillov, D. Schlesinger, W. Forkel, A. Zelenin, S. Zheng et al., A Generic CNN-CRF Model for Semantic Segmentation, pp.1511-05067, 2015.

J. Kittler, Feature set search algorithms " . Pattern recognition and signal processing, 1978.
DOI : 10.1007/978-94-009-9941-1_3

C. Ko, G. Sohn, and T. K. , Tree genera classification with geometric features from high-density airborne LiDAR, Canadian Journal of Remote Sensing, vol.55, issue.1, pp.73-85, 2013.
DOI : 10.1016/j.isprsjprs.2010.08.003

B. Koch, C. Straub, M. Dees, Y. Wang, and H. Weinacker, Airborne laser data for stand delineation and information extraction, International Journal of Remote Sensing, vol.48, issue.4, pp.935-963, 2009.
DOI : 10.1016/j.rse.2004.02.001

R. Kohavi and G. H. John, Wrappers for feature subset selection, Artificial Intelligence, vol.97, issue.1-2, pp.273-324, 1997.
DOI : 10.1016/S0004-3702(97)00043-X
URL : https://doi.org/10.1016/s0004-3702(97)00043-x

P. Kohli, L. Ladický, and P. T. English, Robust Higher Order Potentials for Enforcing Label Consistency, International Journal of Computer Vision, vol.823, pp.302-324, 2009.
DOI : 10.1007/s11263-008-0202-0
URL : http://research.microsoft.com/en-us/um/people/pkohli/papers/klt_IJCV09.pdf

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
URL : http://www.adastral.ucl.ac.uk/~vladkolm/papers/TRW-S-PAMI.pdf

V. Kolmogorov and R. Zabih, What energy functions can be minimized via graph cuts?, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.26, issue.2, pp.147-159, 2004.
DOI : 10.1109/TPAMI.2004.1262177
URL : http://ecommons.cornell.edu/bitstream/1813/5842/1/2001-1857.pdf

V. Kolmogorov and C. Rother, Minimizing non-submodular functions with graph cuts-a review, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.297, pp.1274-1279, 2007.
DOI : 10.1109/tpami.2007.1031
URL : http://www.adastral.ucl.ac.uk/~vladkolm/papers/KR-PAMI07.pdf

P. Kontschieder, M. Fiterau, A. Criminisi, and S. R. Bulo, Deep Neural Decision Forests, 2015 IEEE International Conference on Computer Vision (ICCV), pp.1467-1475, 2015.
DOI : 10.1109/ICCV.2015.172

I. Korpela, H. O. Ørka, M. Maltamo, T. Tokola, and J. Hyyppä, Tree species classification using airborne LiDAR ??? effects of stand and tree parameters, downsizing of training set, intensity normalization, and sensor type, Silva Fennica, vol.44, issue.2, pp.319-339, 2010.
DOI : 10.14214/sf.156

S. Kumar and M. Hebert, Discriminative Random Fields, International Journal of Computer Vision, vol.21, issue.1, pp.179-201, 2006.
DOI : 10.1016/S0262-8856(02)00125-7
URL : http://repository.cmu.edu/cgi/viewcontent.cgi?article=1360&context=robotics

L. '. Ladický, C. Russell, P. Kohli, and P. Torr, Inference Methods for CRFs with Co-occurrence Statistics, International Journal of Computer Vision, vol.103, issue.2, pp.213-225, 2012.
DOI : 10.1109/TPAMI.2007.1036

D. A. Landgrebe, Signal theory methods in multispectral remote sensing, 2005.
DOI : 10.1002/0471723800

L. Landrieu and G. Obozinski, Cut Pursuit: fast algorithms to learn piecewise constant functions, In: Artificial Intelligence and Statistics, pp.1384-1393, 2016.
DOI : 10.1137/17m1113436
URL : https://hal.archives-ouvertes.fr/hal-01306786

R. Larsen, Decomposition using maximum autocorrelation factors, Journal of Chemometrics, vol.28, issue.8-10, pp.8-10, 2002.
DOI : 10.1007/BF02289573
URL : http://www2.imm.dtu.dk/pubdb/views/edoc_download.php/418/pdf/imm418.pdf

L. Moan, S. , A. Mansouri, Y. Voisin, and J. Y. Hardeberg, A constrained band selection method based on information measures for spectral image color visualization, IEEE Transactions on Geoscience and Remote Sensing, vol.4912, pp.5104-5115, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00637700

L. Toan, T. , A. Beaudoin, J. Riom, and D. Guyon, Relating forest biomass to SAR data, IEEE Transactions on Geoscience and Remote Sensing, vol.30, issue.2, pp.403-411, 1992.
DOI : 10.1109/36.134089

D. G. Leckie, F. A. Gougeon, N. Walsworth, and D. Paradine, Stand delineation and composition estimation using semi-automated individual tree crown analysis " . Remote Sensing of Environment 85, pp.355-369, 2003.
DOI : 10.1016/s0034-4257(03)00013-0

N. J. Lee and K. Nakane, Forest Vegetation Classification and Biomass Estimation Based on Landsat TM Data in a Mountainous Region of West Japan, pp.159-171, 1997.
DOI : 10.1007/978-94-011-5446-8_7

T. C. Lemprière, P. Y. Bernier, A. L. Carroll, M. D. Flannigan, R. P. Gilsenan et al., The importance of forest sector adaptation to climate change, Natural Resources Canada, 2008.

V. J. Leppänen, T. Tokola, M. Maltamo, L. Mehtätalo, T. Pusa et al., Automatic delineation of forest stands from LIDAR data, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.5-8, 2008.

A. Levinshtein, A. Stere, K. N. Kutulakos, D. J. Fleet, S. J. Dickinson et al., TurboPixels: Fast Superpixels Using Geometric Flows, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.31, issue.12, pp.2290-2297, 2009.
DOI : 10.1109/TPAMI.2009.96

J. Li, B. Hu, and T. L. Noland, Classification of tree species based on structural features derived from high density LiDAR data, Agricultural and Forest Meteorology 171, pp.104-114, 2013.
DOI : 10.1016/j.agrformet.2012.11.012

S. Li, H. Wu, D. Wan, and J. Zhu, An effective feature selection method for hyperspectral image classification based on genetic algorithm and support vector machine " . Knowledge-Based Systems 24, pp.40-48, 2011.

K. Lim, P. Treitz, M. Wulder, B. St-onge, and M. Flood, LiDAR remote sensing of forest structure, Progress in Physical Geography 27.1, pp.88-106, 2003.
DOI : 10.1007/978-3-540-37456-5

Y. Lin and J. Hyyppä, A comprehensive but efficient framework of proposing and validating feature parameters from airborne LiDAR data for tree species classification, International Journal of Applied Earth Observation and Geoinformation, vol.46, pp.45-55, 2016.
DOI : 10.1016/j.jag.2015.11.010

H. Liu and R. Setiono, Feature selection and classification-a probabilistic wrapper approach, Proceedings of 9th International Conference on Industrial and Engineering Applications of AI and ES, pp.419-424, 1997.

L. Liu, Y. Pang, W. Fan, Z. Li, and M. Li, Fusion of airborne hyperspectral and LiDAR data for tree species classification in the temperate forest of northeast China, 2011 19th International Conference on Geoinformatics, pp.1-5, 2011.
DOI : 10.1109/GeoInformatics.2011.5981118

D. Lu and Q. Weng, A survey of image classification methods and techniques for improving classification performance, International Journal of Remote Sensing, vol.61, issue.5, pp.823-870, 2007.
DOI : 10.1016/S0034-4257(01)00305-4

A. Lucchi, Y. Li, X. Boix, K. Smith, and P. Fua, Are spatial and global constraints really necessary for segmentation?, 2011 International Conference on Computer Vision, pp.9-16, 2011.
DOI : 10.1109/ICCV.2011.6126219

A. Maas, F. Rottensteiner, and C. Heipke, Using label noise robust logistic regression for automated updating of topographic geospatial databases, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences III-7, pp.133-140, 2016.

C. Mallet, F. Bretar, M. Roux, U. Soergel, and C. Heipke, Relevance assessment of fullwaveform lidar data for urban area classification, ISPRS Journal of Photogrammetry and Remote Sensing, vol.666, pp.71-84, 2011.

T. Marill and D. Green, On the effectiveness of receptors in recognition systems, IEEE Transactions on Information Theory, vol.9, issue.1, pp.11-17, 1963.
DOI : 10.1109/TIT.1963.1057810

A. Martínez-usó, F. Pla, J. M. Sotoca, and P. García-sevilla, Clustering-based hyperspectral band selection using information measures, IEEE Transactions on Geoscience and Remote Sensing, vol.4512, pp.4158-4171, 2007.

P. Mather and B. Tso, Classification methods for remotely sensed data, 2016.

J. E. Means, S. A. Acker, B. J. Fitt, M. Renslow, L. Emerson et al., Predicting forest stand characteristics with airborne scanning lidar " . Photogrammetric Engineering & Remote Sensing 66, pp.1367-1372, 2000.

A. Mellor, A. Haywood, C. Stone, and S. Jones, The Performance of Random Forests in an Operational Setting for Large Area Sclerophyll Forest Classification, Remote Sensing, vol.68, issue.6, pp.2838-2856, 2013.
DOI : 10.1117/1.3216031

A. Mellor, S. Boukir, A. Haywood, and S. Jones, Using ensemble margin to explore issues of training data imbalance and mislabeling on large area land cover classification, 2014 IEEE International Conference on Image Processing (ICIP), pp.5067-5071, 2014.
DOI : 10.1109/ICIP.2014.7026026

A. P. Moore, S. J. Prince, J. Warrell, U. Mohammed, and G. Jones, Superpixel lattices, 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp.1-8, 2008.
DOI : 10.1109/CVPR.2008.4587471

B. Mora, M. A. Wulder, and J. C. White, Segment-constrained regression tree estimation of forest stand height from very high spatial resolution panchromatic imagery over a boreal environment, Remote Sensing of Environment, vol.114, issue.11, pp.2474-2484, 2010.
DOI : 10.1016/j.rse.2010.05.022

P. Moritz, R. Nishihara, I. Stoica, and M. I. Jordan, Sparknet: Training deep networks in spark, 2015.

F. Morsdorf, E. Meier, B. Allgöwer, and D. Nüesch, Clustering in airborne laser scanning raw data for segmentation of single trees " . International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 34, p.13, 2003.

G. Moser, S. B. Serpico, and J. A. Benediktsson, Land-Cover Mapping by Markov Modeling of Spatial???Contextual Information in Very-High-Resolution Remote Sensing Images, Proceedings of the IEEE 101.3, pp.631-651, 2013.
DOI : 10.1109/JPROC.2012.2211551

P. Muukkonen, Generalized allometric volume and biomass equations for some tree species in Europe, European Journal of Forest Research, vol.187, issue.2, pp.157-166, 2007.
DOI : 10.1016/j.foreco.2003.07.007

E. Naesset, Predicting forest stand characteristics with airborne scanning laser using a practical two-stage procedure and field data " . Remote Sensing of Environment 80, pp.88-99, 2002.

A. Nguyen and B. Le, 3D point cloud segmentation: A survey, 2013 6th IEEE Conference on Robotics, Automation and Mechatronics (RAM), pp.225-230, 2013.
DOI : 10.1109/RAM.2013.6758588

M. Nitzberg, D. Mumford, and T. Shiota, Filtering, segmentation and depth, 1993.
DOI : 10.1007/3-540-56484-5

W. Ouerghemmi, A. Le-bris, N. Chehata, and C. Mallet, A TWO-STEP DECISION FUSION STRATEGY: APPLICATION TO HYPERSPECTRAL AND MULTISPECTRAL IMAGES FOR URBAN CLASSIFICATION, ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.1, issue.1, 2017.
DOI : 10.5194/isprs-archives-XLII-1-W1-167-2017

S. Paisitkriangkrai, J. Sherrah, P. Janney, and V. D. Hengel, Effective semantic pixel labelling with convolutional networks and Conditional Random Fields, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.36-43, 2015.
DOI : 10.1109/CVPRW.2015.7301381

S. Paisitkriangkrai, J. Sherrah, P. Janney, A. Van-den, and . Hengel, Semantic Labeling of Aerial and Satellite Imagery, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.9, issue.7, pp.2868-2881, 2016.
DOI : 10.1109/JSTARS.2016.2582921

M. Pal, Random forest classifier for remote sensing classification, International Journal of Remote Sensing, vol.20, issue.1, pp.217-222, 2005.
DOI : 10.1016/S0034-4257(03)00132-9

N. R. Pal and S. K. , A review on image segmentation techniques, Pattern Recognition, vol.26, issue.9, pp.1277-1294, 1993.
DOI : 10.1016/0031-3203(93)90135-J

Y. Pan, R. A. Birdsey, O. L. Phillips, and R. B. Jackson, The Structure, Distribution, and Biomass of the World's Forests, Annual Review of Ecology, Evolution, and Systematics, vol.44, issue.1, pp.593-622, 2013.
DOI : 10.1146/annurev-ecolsys-110512-135914

J. D. Paola and R. A. Schowengerdt, A review and analysis of backpropagation neural networks for classification of remotely-sensed multi-spectral imagery, International Journal of Remote Sensing, vol.55, issue.16, pp.3033-3058, 1995.
DOI : 10.1109/36.312899

S. Paris, P. Kornprobst, J. Tumblin, and F. Durand, Bilateral filtering: theory and applications " . Foundations and Trends in Computer Graphics and Vision 4, pp.1-73, 2009.
DOI : 10.1561/0600000020

S. Paris and F. Durand, A fast approximation of the bilateral filter using a signal processing approach, International Journal of Computer Vision, vol.811, pp.24-52, 2009.

G. Patenaude, R. Milne, and T. P. Dawson, Synthesis of remote sensing approaches for forest carbon estimation: reporting to the Kyoto Protocol, Environmental Science & Policy, vol.8, issue.2, pp.161-178, 2005.
DOI : 10.1016/j.envsci.2004.12.010

T. Peli and D. Malah, A study of edge detection algorithms, Computer Graphics and Image Processing, vol.201, pp.1-21, 1982.

C. Pelletier, S. Valero, J. Inglada, N. Champion, and G. Dedieu, Assessing the robustness of Random Forests to map land cover with high resolution satellite image time series over large areas, Remote Sensing of Environment, vol.187, pp.156-168, 2016.
DOI : 10.1016/j.rse.2016.10.010

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
URL : https://authors.library.caltech.edu/6498/1/PERieeetpami90.pdf

A. Pommerening, Approaches to quantifying forest structures, Forestry, vol.75, issue.3, pp.305-324, 2002.
DOI : 10.1093/forestry/75.3.305
URL : https://academic.oup.com/forestry/article-pdf/75/3/305/6746452/750305.pdf

T. Postadjian, A. Le-bris, H. Sahbi, and C. Mallet, Investigatin the potential of deep neural networks for large-scale classification of very high resolution satellite images, ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, vol.4, 2017.

P. Pudil, J. Novovi?ová, and J. Kittler, Floating search methods in feature selection, Pattern Recognition Letters, vol.15, issue.11, pp.1119-1125, 1994.
DOI : 10.1016/0167-8655(94)90127-9

J. Radoux and P. Defourny, A quantitative assessment of boundaries in automated forest stand delineation using very high resolution imagery " . Remote Sensing of Environment 110, pp.468-475, 2007.

J. Radoux, C. Lamarche, E. Van-bogaert, S. Bontemps, C. Brockmann et al., Automated Training Sample Extraction for Global Land Cover Mapping, Remote Sensing, vol.42, issue.5, p.3965, 2014.
DOI : 10.1109/TGRS.2004.827257
URL : http://www.mdpi.com/2072-4292/6/5/3965/pdf

M. H. Ramage, H. Burridge, M. Busse-wicher, G. Fereday, T. Reynolds et al., The wood from the trees: The use of timber in construction, Renewable and Sustainable Energy Reviews, vol.68, pp.333-359, 2017.
DOI : 10.1016/j.rser.2016.09.107

J. Reitberger, C. Schnörr, P. Krzystek, and U. Stilla, 3D segmentation of single trees exploiting full waveform LIDAR data, ISPRS Journal of Photogrammetry and Remote Sensing, vol.64, issue.6, pp.561-574, 2009.
DOI : 10.1016/j.isprsjprs.2009.04.002

J. J. Rodriguez, L. I. Kuncheva, and C. J. Alonso, Rotation Forest: A New Classifier Ensemble Method, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, issue.10, pp.1619-1630, 2006.
DOI : 10.1109/TPAMI.2006.211

R. Ronfard, Region-based strategies for active contour models, International Journal of Computer Vision, vol.23, issue.3, pp.229-251, 1994.
DOI : 10.1007/BF01427153
URL : https://hal.archives-ouvertes.fr/inria-00544609

P. Salembier and L. Garrido, Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval, IEEE Transactions on Image Processing, vol.9, issue.4, pp.561-576, 2000.
DOI : 10.1109/83.841934

K. Schindler, An Overview and Comparison of Smooth Labeling Methods for Land-Cover Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.50, issue.11, pp.4534-4545, 2012.
DOI : 10.1109/TGRS.2012.2192741

B. Scholkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization , and beyond, 2001.

A. Schuck, R. Päivinen, T. Hytönen, and B. Pajari, Compilation of forestry terms and definitions, European Forest Institute, 2002.

S. B. Serpico and G. Moser, Extraction of Spectral Channels From Hyperspectral Images for Classification Purposes, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.2, pp.484-495, 2007.
DOI : 10.1109/TGRS.2006.886177

J. Shan and C. K. Toth, Topographic laser ranging and scanning: principles and processing, 2008.
DOI : 10.1201/9781420051438

Y. A. Sheikh, E. A. Khan, and T. Kanade, Mode-seeking by Medoidshifts, 2007 IEEE 11th International Conference on Computer Vision, pp.1-8, 2007.
DOI : 10.1109/ICCV.2007.4408978

J. Shi and J. Malik, Normalized cuts and image segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), vol.228, pp.888-905, 2000.

J. Smeeckaert, C. Mallet, N. David, N. Chehata, and A. Ferraz, Large-scale classification of water areas using airborne topographic lidar data, Remote Sensing of Environment, vol.138, issue.109, pp.134-148, 2013.
DOI : 10.1016/j.rse.2013.07.004

J. M. Smith and D. Smith, Database abstractions: aggregation and generalization, ACM Transactions on Database Systems (TODS) 2.2, pp.105-133, 1977.

W. H. Smith, Air pollution and forests: interactions between air contaminants and forest ecosystems, 2012.

J. M. Sotoca and F. Pla, Supervised feature selection by clustering using conditional mutual information-based distances " . Pattern Recognition 43, pp.2068-2081, 2010.

J. Souchon, C. Thom, C. Meynard, and O. Martin, A large format camera system for national mapping purposes " . Revue Française de Photogrammétrie et de Télédétection, pp.48-53, 0200.

F. S. Sterrett, Alternative fuels and the environment, 1994.

A. H. Strahler, The use of prior probabilities in maximum likelihood classification of remotely sensed data " . Remote sensing of Environment 10, pp.135-163, 1980.

C. Strobl, A. Boulesteix, A. Zeileis, and T. Hothorn, Bias in random forest variable importance measures: Illustrations, sources and a solution, BMC Bioinformatics, vol.8, issue.1, p.25, 2007.
DOI : 10.1186/1471-2105-8-25

J. Strunk, H. Temesgen, H. Andersen, J. P. Flewelling, and L. Madsen, Effects of lidar pulse density and sample size on a model-assisted approach to estimate forest inventory variables, Canadian Journal of Remote Sensing, vol.112, issue.4, 2012.
DOI : 10.1139/X07-104

D. Stutz, A. Hermans, and B. Leibe, Superpixels: An evaluation of the state-of-the-art, Computer Vision and Image Understanding, vol.166, 2017.
DOI : 10.1016/j.cviu.2017.03.007

A. A. Sullivan, R. J. Mcgaughey, H. Andersen, and P. Schiess, Object-oriented classification of forest structure from light detection and ranging data for stand mapping, Western Journal of Applied Forestry, vol.244, issue.19, pp.198-204, 2009.

J. Tang, S. Alelyani, and H. Liu, Feature selection for classification: A review " . Data Classification: Algorithms and Applications, p.37, 2014.

T. Taxt, P. J. Flynn, and A. K. Jain, Segmentation of document images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.1112, pp.1322-1329, 1989.

S. Thelandersson and H. J. Larsen, Timber engineering, 2003.

D. Tiede, T. Blaschke, and M. Heurich, Object-based semi automatic mapping of forest stands with Laser scanner and Multi-spectral data " . International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.36, issue.19, pp.328-333, 2004.

G. Tochon, Hierarchical analysis of multimodal images, 2015.
URL : https://hal.archives-ouvertes.fr/tel-01242836

G. Tochon, J. B. Féret, S. Valero, R. E. Martin, D. E. Knapp et al., On the use of binary partition trees for the tree crown segmentation of tropical rainforest hyperspectral images, Remote Sensing of Environment, vol.159, pp.318-331, 2015.
DOI : 10.1016/j.rse.2014.12.020

P. Tokarczyk, J. D. Wegner, S. Walk, and K. Schindler, Features, Color Spaces, and Boosting: New Insights on Semantic Classification of Remote Sensing Images, IEEE Transactions on Geoscience and Remote Sensing, vol.53, issue.1, pp.280-295, 2015.
DOI : 10.1109/TGRS.2014.2321423

T. Tokola, Remote Sensing Concepts and Their Applicability in REDD+ Monitoring, Current Forestry Reports, vol.115, issue.2014, pp.252-260, 2015.
DOI : 10.1016/j.rse.2010.10.013

E. Tomppo, H. Olsson, G. Ståhl, M. Nilsson, O. Hagner et al., Combining national forest inventory field plots and remote sensing data for forest databases " . Remote Sensing of Environment 112, pp.1982-1999, 2008.
DOI : 10.1016/j.rse.2007.03.032

H. Torabzadeh, F. Morsdorf, and M. E. Schaepman, Fusion of imaging spectroscopy and airborne laser scanning data for characterization of forest ecosystems ??? A review, ISPRS Journal of Photogrammetry and Remote Sensing, vol.97, pp.25-35, 2014.
DOI : 10.1016/j.isprsjprs.2014.08.001

H. Torabzadeh, R. Leiterer, M. E. Schaepman, and F. Morsdorf, Optimal structural and spectral features for tree species classification using combined airborne laser scanning and hyperspectral data, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.5399-5402, 2015.
DOI : 10.1109/IGARSS.2015.7327056
URL : http://www.zora.uzh.ch/id/eprint/116917/1/07327056.pdf

R. Trias-sanz, Semi-automatic rural land cover classification from high-resolution remote sensing images, Université Paris, vol.5, 2006.

C. J. Tucker, Red and photographic infrared linear combinations for monitoring vegetation " . Remote sensing of Environment 8, pp.127-150, 1979.
DOI : 10.1016/0034-4257(79)90013-0
URL : http://hdl.handle.net/2060/19780024582

D. Tuia, F. Pacifici, M. Kanevski, and W. J. Emery, Classification of Very High Spatial Resolution Imagery Using Mathematical Morphology and Support Vector Machines, IEEE Transactions on Geoscience and Remote Sensing, vol.47, issue.11, pp.3866-3879, 2009.
DOI : 10.1109/TGRS.2009.2027895

D. Tuia, M. Volpi, and G. Moser, Getting pixels and regions to agree with conditional random fields, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.5399-5402, 2016.
DOI : 10.1109/IGARSS.2016.7729851

A. Ullrich and M. Pfennigbauer, Linear LIDAR versus Geiger-mode LIDAR: impact on data properties and data quality, SPIE Defense+ Security. International Society for Optics and Photonics, pp.983204-983204, 2016.
DOI : 10.1117/12.2223586

V. Vapnik, The nature of statistical learning theory, 2013.

J. Vauhkonen, I. Korpela, M. Maltamo, and T. Tokola, Imputation of single-tree attributes using airborne laser scanning-based height, intensity, and alpha shape metrics " . Remote Sensing of Environment 114, pp.1263-1276, 2010.

J. Vauhkonen, T. Hakala, J. Suomalainen, S. Kaasalainen, O. Nevalainen et al., Classification of Spruce and Pine Trees Using Active Hyperspectral LiDAR, IEEE Geoscience and Remote Sensing Letters 10.5, pp.1138-1141, 2013.
DOI : 10.1109/LGRS.2012.2232278

A. Vedaldi and S. Soatto, Quick Shift and Kernel Methods for Mode Seeking, Computer vision? ECCV 2008, pp.705-718, 2008.
DOI : 10.1007/978-3-540-88693-8_52

C. Véga, A. Hamrouni, S. Mokhtari, J. Morel, J. Bock et al., PTrees: A point-based approach to forest tree extraction from lidar data, International Journal of Applied Earth Observation and Geoinformation, vol.33, issue.26, pp.98-108, 2014.
DOI : 10.1016/j.jag.2014.05.001

O. Veksler, Y. Boykov, and P. Mehrani, Superpixels and Supervoxels in an Energy Optimization Framework, Computer Vision?ECCV 2010, pp.211-224, 2010.
DOI : 10.1007/978-3-642-15555-0_16

L. Vincent and P. Soille, Watersheds in digital spaces: an efficient algorithm based on immersion simulations, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.13, issue.6, pp.583-598, 1991.
DOI : 10.1109/34.87344

M. Viterbini, A. Adriani, and G. D. Donfrancesco, Single photon detection and timing system for a lidar experiment " . Review of scientific instruments 58, pp.1833-1839, 1987.

K. A. Vogt, J. M. Honea, D. J. Vogt, T. Patel-weynand, R. L. Edmonds et al., Global societies and forest legacies creating today's forest landscapes Forests and society: sustainability and life cycles of forests in human landscapes, pp.30-59, 2006.

M. Volpi and V. Ferrari, Semantic segmentation of urban scenes by learning local class interactions, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.1-9, 2015.
DOI : 10.1109/CVPRW.2015.7301377

G. Vosselman and H. Maas, Airborne and terrestrial laser scanning, 2010.

H. Ferraz, F. Torabzadeh, L. Morsdorf, J. Zhu, P. Liu et al., International Benchmarking of the Individual Tree Detection Methods for Modeling 3D Canopy Structure for Silviculture and Forest Ecology Using Airborne Laser Scanning, IEEE Transactions on Geoscience and Remote Sensing, vol.154, issue.64, pp.501-502, 2016.

Z. Wang, R. Boesch, and C. Ginzler, Forest delineation of aerial images with Gabor wavelets, International Journal of Remote Sensing, vol.22, issue.7, pp.2196-2213, 2012.
DOI : 10.1016/j.rse.2004.07.009

M. Wasiq and M. Ahmad, Sustaining forests: A development strategy, 2004.

J. D. Wegner, J. A. Montoya-zegarra, and K. Schindler, A Higher-Order CRF Model for Road Network Extraction, 2013 IEEE Conference on Computer Vision and Pattern Recognition, pp.1698-1705, 2013.
DOI : 10.1109/CVPR.2013.222
URL : http://www.cv-foundation.org/openaccess/content_cvpr_2013/papers/Wegner_A_Higher-Order_CRF_2013_CVPR_paper.pdf

J. D. Wegner, S. Branson, D. Hall, K. Schindler, and P. Perona, Cataloging Public Objects Using Aerial and Street-Level Images ??? Urban Trees, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.6014-6023, 2016.
DOI : 10.1109/CVPR.2016.647

M. Weinmann, Reconstruction and Analysis of 3D Scenes, pp.978-981, 2016.
DOI : 10.1007/978-3-319-29246-5

M. Weinmann, B. Jutzi, S. Hinz, and C. Mallet, Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers, ISPRS Journal of Photogrammetry and Remote Sensing, vol.105, pp.286-304, 2015.
DOI : 10.1016/j.isprsjprs.2015.01.016

J. C. White, N. C. Coops, M. A. Wulder, M. Vastaranta, T. Hilker et al., Remote Sensing Technologies for Enhancing Forest Inventories: A Review, Canadian Journal of Remote Sensing, vol.4, issue.5, pp.619-641, 2016.
DOI : 10.1016/j.isprsjprs.2014.12.013

A. W. Whitney, A Direct Method of Nonparametric Measurement Selection, IEEE Transactions on Computers, vol.20, issue.9, pp.1100-1103, 1971.
DOI : 10.1109/T-C.1971.223410

R. Wilson and M. Spann, Image segmentation and uncertainty, 1988.

S. Workman, M. Zhai, J. David, N. Crandall, and . Jacobs, A Unified Model for Near and Remote Sensing, 2017 IEEE International Conference on Computer Vision (ICCV), 2017.
DOI : 10.1109/ICCV.2017.293

Z. Wu, V. Heikkinen, M. Hauta-kasari, J. Parkkinen, and T. Tokola, ALS data based forest stand delineation with a coarse-to-fine segmentation approach, 2014 7th International Congress on Image and Signal Processing, pp.547-552, 2014.
DOI : 10.1109/CISP.2014.7003840

M. A. Wulder, C. W. Bater, N. C. Coops, T. Hilker, and J. C. White, The role of LiDAR in sustainable forest management " . The Forestry Chronicle 84, pp.807-826, 2008.

M. A. Wulder, J. C. White, G. J. Hay, and G. Castilla, Towards automated segmentation of forest inventory polygons on high spatial resolution satellite imagery " . The Forestry Chronicle 84, pp.221-230, 2008.

H. Yang, S. Zhang, K. Deng, and P. Du, Research into a Feature Selection Method for Hyperspectral Imagery Using PSO and SVM, Journal of China University of Mining and Technology, vol.17, issue.4, pp.473-478, 2007.
DOI : 10.1016/S1006-1266(07)60128-X

S. D. Yanowitz and A. M. Bruckstein, A new method for image segmentation " . Computer Vision, Graphics and Image Processing 46, pp.82-95, 1989.

A. Zargar, R. Sadiq, B. Naser, and F. I. Khan, A review of drought indices, Environmental Reviews, vol.34, issue.20, pp.333-349, 2011.
DOI : 10.1016/j.jhydrol.2008.04.004

L. Zhang, Y. Zhong, B. Huang, J. Gong, and P. Li, Dimensionality Reduction Based on Clonal Selection for Hyperspectral Imagery, IEEE Transactions on Geoscience and Remote Sensing, vol.45, issue.12, pp.4172-4186, 2007.
DOI : 10.1109/TGRS.2007.905311

Y. Zhang, Advances in image and video segmentation, 2006.
DOI : 10.4018/978-1-59140-753-9

L. Zhuo, J. Zheng, X. Li, F. Wang, B. Ai et al., A genetic algorithm based wrapper feature selection method for classification of hyperspectral images using support vector machine, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Classification of Remote Sensing Images, 2008.
DOI : 10.1117/12.813256