, Résultats de segmentation sémantique et d'apprentissage par transfert pour le jeu de données ISPRS Vaihingen

, Taxonomie des types d'occupation des sols de UrbanAtlas, p.154, 2012.

, Performances de segmentation sémantique d'un modèle SegNet sur MiniFrance154

, Comparaison des statistiques au niveau pixel entre Vaihingen et MiniFrance, p.155

, Nombre de véhicules par classe dans les différents jeux de données, p.163

, Résultats de segmentation sémantique sur le jeu de données ISPRS Potsdam à 12,5 cm/px

, Résultats de segmentation sémantique sur NZAM/ONERA Christchurch, p.165

, Segmentation d'instance et détection de véhicules pour différents prétraite-ments morphologiques

R. De-détection-de-véhicules-sur-potsdam and . .. Christchurch, , p.166

, Erreur moyenne d'estimation du nombre de véhicules par cellule de 125 m 2 × 125 m 2, p.167

. .. , Résultats de classification de plusieurs CNN sur VEDAI (en %), p.169

, AlexNet sur VEDAI avec plusieurs prétraitements

, Résultats de classification de véhicules sur les vérités terrain augmentées de Potsdam et Christchurch

, Résultats de validation croisée sur les jeux de données ISPRS (multitâche), p.174

S. Résultats-sur-le-jeu-de-données and . Rgb-d, , p.177

, Résultats de segmentation sémantique sur le jeu de données DFC, p.178, 2015.

, Performances de segmentation sémantique sur le jeu de données CamVid, p.178

P. H. Jón-atli-benediktsson, . Swain, K. Okan, and . Ersoy, « Neural Network Approaches Versus Statistical Methods In Classification Of Multisource Remote Sensing Data, IEEE Transactions on Geoscience and Remote Sensing, vol.28, p.41

Y. Bengio, Foundations and trends® in Machine Learning, vol.2, p.12, 2009.

Y. Bengio, Practical Recommendations for Gradient-Based Training of Deep Architectures, p.20, 2012.

Y. Bengio, Advances in Neural Information Processing Systems 19. Sous la dir, vol.19, p.12, 2007.

M. Bianchini and F. Scarselli, On the Complexity of Neural Network Classifiers : A Comparison Between Shallow and Deep Architectures, vol.25, p.16

M. Boden, Mind as Machine : A History of Cognitive Science, p.28

B. E. Boser, I. M. Guyon, and V. Vapnik, A Training Algorithm for Optimal Margin Classifiers, Proceedings of the Fifth Annual Workshop on Computational Learning Theory. COLT '92, p.41, 1992.

P. Bosilj, Partition and Inclusion Hierarchies of Images : A Comprehensive Survey, vol.4, p.43, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01848445

L. Bottou, « Large-Scale Machine Learning with Stochastic Gradient Descent, COMPSTAT. 2010 (cf, p.41
DOI : 10.1201/b11429-4

L. Bottou, Neural Networks : Tricks of the Trade. Lecture Notes in Computer Science, vol.20, p.19, 2012.

L. Breiman, Machine Learning, vol.45, p.41, 2001.

L. Breiman, Classification and Regression Trees, Routledge, p.41, 2017.

J. Gabriel, J. Brostow, R. Fauqueur, and . Cipolla, Video-based Object and Event Analysis, Semantic Object Classes in Video : A High-Definition Ground Truth Database, vol.30, pp.167-8655, 2009.

A. Louis and C. , Comptes rendus hebdomadaires des séances de l'Académie des sciences. ark :/12148/bpt6k2982c. Paris : Gauthier-Villars, juil. 1847, p.16

K. Chatfield, British Machine Vision Association, Proceedings of the British Machine Vision Conference. British Machine Vision Conference (BMVC), p.31, 2014.

K. Chellapilla, S. Puri, and P. Simard, Tenth International Workshop on Frontiers in Handwriting Recognition. Suvisoft, p.12, 2006.

L. Chen, Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs, vol.40, p.35

Y. Chen, Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks, vol.54, p.42, 2016.

F. Chollet and . Xception, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, United States, vol.33, p.32, 2017.

D. C. Cire?an, U. Meier, and J. Schmidhuber, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.12, 2012.

D. C. Cire?an, Deep Big Simple Neural Nets Excel on Handwritten Digit Recognition, p.12

D. C. Cire?an, Deep Neural Networks Segment Neuronal Membranes in Electron Microscopy Images, p.34, 2012.

D. Clevert, T. Unterthiner, and S. Hochreiter, Proceedings of the International Conference on Learning Representations (ICLR). 23 nov, p.15, 2015.

M. Cordts, Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, vol.36, p.34, 2016.

C. Cortes and V. Vapnik, er sept. 1995), Support-Vector Networks, vol.20, pp.885-6125

D. Crevier, AI : The Tumultuous History of the Search for Artificial Intelligence, p.28, 1993.

Y. Cui, Laetitia Chapel et Sébastien Lefèvre. « Scalable Bag of Subpaths Kernel for Learning on Hierarchical Image Representations and Multi-Source Remote Sensing Data Classification, vol.9, p.196

G. Cybenko, Signals and Systems 2.4 (1 er déc, Approximation by Superpositions of a Sigmoidal Function, vol.15, p.11, 1989.

, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Juin, vol.22, p.34, 2005.

. Mauro-dalla-mura, Morphological Attribute Profiles for the Analysis of Very High Resolution Images, IEEE Transactions on Geoscience and Remote Sensing, vol.48, p.40, 2010.

I. Daubechies, Ten Lectures on Wavelets. Philadelphia, PA, USA : Society for Industrial and Applied Mathematics, 1992.

. Guillaume-de-l'hôpital, Analyse des infiniment petits, pour l'intelligence des lignes courbes. Paris : Montalant, 1716, vol.227, p.17

C. Dechesne, Semantic Segmentation of Forest Stands of Pure Species Combining Airborne Lidar Data and Very High Resolution Multispectral Imagery, Journal of Photogrammetry and Remote Sensing, vol.126, pp.924-2716

J. Deng, A Large-Scale Hierarchical Image Database, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Juin, vol.30, p.28, 2009.
DOI : 10.1109/cvpr.2009.5206848

URL : http://www.image-net.org/papers/imagenet_cvpr09.pdf

C. Dong, Chen Change Loy et Xiaoou Tang. « Accelerating the Super-Resolution Convolutional Neural Network, p.25, 2016.

J. Duchi, E. Hazan, and Y. Singer, Adaptive Subgradient Methods for Online Learning and Stochastic Optimization, Journal of Machine Learning Research, vol.12, pp.2121-2159, 2011.

F. Vincent-dumoulin and . Visin, « A Guide to Convolution Arithmetic for Deep Learning, vol.24, p.23, 2016.

M. Everingham, The Pascal Visual Object Classes Challenge : A Retrospective, vol.111, pp.34-36

M. Fauvel, Advances in Spectral-Spatial Classification of Hyperspectral Images, vol.101, p.40, 2013.
URL : https://hal.archives-ouvertes.fr/hal-00737075

D. Grangier, L. Bottou, and R. Collobert, ICML 2009 Deep Learning Workshop. T. 3. Citeseer, p.34, 2009.

C. Gu, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Juin, p.34, 2009.

L. Guo, « Relevance of Airborne Lidar and Multispectral Image Data for Urban Scene Classification Using Random Forests, Journal of Photogrammetry and Remote Sensing, vol.66, p.40, 2011.

J. Ham, « Investigation of the Random Forest Framework for Classification of Hyperspectral Data, IEEE Transactions on Geoscience and Remote Sensing, vol.43, p.40, 2005.

K. He, Proceedings of the IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision (ICCV). Déc, vol.19, p.15, 2015.

K. He, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, United States, vol.33, p.32, 2016.

D. O. Hebb, The Organization of Behavior. 1949. pmid : 10643472 (cf, p.11

G. E. Hinton, S. Osindero, and Y. Teh, A Fast Learning Algorithm for Deep Belief Nets, Neural Computation 18.7 (juil. 2006), vol.19, p.12

G. E. Hinton and R. Salakhutdinov, Reducing the Dimensionality of Data with Neural Networks, pp.504-507

S. Hochreiter, Gradient Flow in Recurrent Nets : The Difficulty of Learning Long-Term Dependencies, p.15, 2001.

K. Hornik, Approximation Capabilities of Multilayer Feedforward Networks, vol.4, p.11, 1991.

A. G. Howard, MobileNets : Efficient Convolutional Neural Networks for Mobile Vision Applications, p.34, 2017.

B. Huang, Scale Semantic Classification : Outcome of the First Year of Inria Aerial Image Labeling Benchmark, 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 22 juil, p.43, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01767807

Y. Fu-jie-huang and . Lecun, « Large-Scale Learning with SVM and Convolutional for Generic Object Categorization, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Juin, p.12, 2006.

A. Lagrange, Benchmarking Classification of Earth-Observation Data : From Learning Explicit Features to Convolutional Networks, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Juil, p.42, 2015.

J. Lagrange, Théorie des fonctions analytiques, contenant les principes du calcul différentiel , dégagés de toute considération d'infiniment petits ou d'évanouissans, de limites ou de fluxions, et réduits a l'analyse algébrique des quantités finies. À Paris, de l'Imprimerie de la République. Prairial an V

D. J. Lary, Geoscience Frontiers. Special Issue : Progress of Machine Learning in Geosciences, vol.7, pp.1674-9871, 2016.

R. Lawrence, Classification of Remotely Sensed Imagery Using Stochastic Gradient Boosting as a Refinement of Classification Tree Analysis, pp.331-336, 2004.

A. Le-guennec, Classification de données LiDAR bi-spectral topo-bathymétriques par une approche multi-échelle : Application en milieu fluvial, Conférence Annuelle Française de Photogrammétrie et Télédétection (CFPT), p.40, 2018.

, Yann LeCun. « Learning Process in an Asymmetric Threshold Network, vol.16, p.11, 1986.

Y. Lecun, Backpropagation Applied to Handwritten Zip Code Recognition, Neural Computation 1.4 (déc. 1989), p.12

Y. Lecun, Neural Networks : Tricks of the Trade. Lecture Notes in Computer Science, vol.17, pp.9-50, 1998.

Y. Lecun, Gradient-Based Learning Applied to Document Recognition, Proceedings of the IEEE, vol.86, p.28, 1998.

C. Lee, Deeply-Supervised Nets, Artificial Intelligence and Statistics. Artificial Intelligence and Statistics, vol.21, p.31, 2015.

J. Y. Lettvin, What the Frog's Eye Tells the Frog's Brain, Proceedings of the IRE 47.11 (nov. 1959), vol.13, p.12

A. Li, Lidar Aboveground Vegetation Biomass Estimates in Shrublands : Prediction, Uncertainties and Application to Coarser Scales, vol.9, p.903

H. W. Lin, M. Tegmark, and D. Rolnick, « Why Does Deep and Cheap Learning Work So Well ?, Journal of Statistical Physics, vol.168, p.16
DOI : 10.1007/s10955-017-1836-5

URL : http://arxiv.org/pdf/1608.08225

T. Lin, Sous la dir. de David Fleet et al. Lecture Notes in Computer Science 8693, Common Objects in Context, vol.36, p.34, 2014.

C. Liu, Chinese Handwriting Recognition Competition, 2011 International Conference on Document Analysis and Recognition. 2011 International Conference on Document Analysis and Recognition, vol.28, p.12, 2011.

S. Liu, « Path Aggregation Network for Instance Segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

T. Liu, Support Vector Machine, and Patch-Based Deep Convolutional Neural Networks for ObjectBased Wetland Mapping Using Images from Small Unmanned Aircraft System, Comparing Fully Convolutional Networks, Random Forest, vol.55, p.43, 2018.

W. Liu, Single Shot MultiBox Detector, Computer Vision -ECCV 2016. European Conference on Computer Vision
DOI : 10.1007/978-3-319-46448-0_2

URL : http://arxiv.org/pdf/1512.02325

C. Springer, , p.34, 2016.

Y. Liu, Dense Semantic Labeling of Very-High-Resolution Aerial Imagery and LiDAR with Fully-Convolutional Neural Networks and Higher-Order CRFs, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Honolulu, United States, p.43, 2017.

J. Long, E. Shelhamer, and T. Darrell, « Fully Convolutional Networks for Semantic Segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Juin, vol.43, pp.34-36, 2015.

I. Loshchilov, F. «. Hutter, and . Sgdr, Stochastic Gradient Descent with Warm Restarts, Proceedings of the International Conference on Learning Representations (ICLR), p.19, 2017.

D. G. Lowe, Proceedings of the Seventh IEEE International Conference on Computer Vision. Proceedings of the Seventh IEEE International Conference on Computer Vision. T. 2. 1999, vol.2, p.22

Z. Lu, Sous la dir. d'I. Guyon et al, The Expressive Power of Neural Networks : A View from the Width, vol.30, pp.6231-6239, 2017.

A. L. Maas, Y. Awni, A. Y. Hannun, and . Ng, ICML Workshop on Deep Learning for Audio, Speech and Language Processing, p.15, 2013.

S. Mallat, Une exploration des signaux en ondelettes. Palaiseau : Éditions de l'École polytechnique, 12 sept, vol.637, p.22, 2001.

S. Mar?celjamar?celja, Mathematical Description of the Responses of Simple Cortical Cells, vol.70, p.22, 1980.

D. Marmanis, « Classification With an Edge : Improving Semantic Image Segmentation with Boundary Detection, ISPRS Journal of Photogrammetry and Remote Sensing, p.43, 2017.

S. Warren, . Mcculloch, H. Walter, and . Pitts, « A Logical Calculus of the Ideas Immanent in Nervous Activity, Bulletin of Mathematical Biophysics, vol.5, p.10, 1943.

F. Melgani and L. Bruzzone, « Classification of Hyperspectral Remote Sensing Images with Support Vector Machines, IEEE Transactions on Geoscience and Remote Sensing, vol.42, p.41, 2004.

H. Mhaskar, Q. Liao, A. Tomaso, and . Poggio, « When and Why Are Deep Networks Better than Shallow Ones ?, p.16, 2017.

M. Minsky, A. Seymour, . Papert, and . Perceptrons, , p.11, 1969.

, Machine Learning for Aerial Image Labeling, p.42, 2013.

H. Moravec, Mind Children -The Future of Robot & Human Intelligence, vol.224, p.28, 1988.

V. Nair and G. E. Hinton, « Rectified Linear Units Improve Restricted Boltzmann Machines, Proceedings of the 27th International Conference on Machine Learning (ICML-10), p.15, 2010.

V. Nekrasov, J. Ju, and J. Choi, Global Deconvolutional Networks for Semantic Segmentation, vol.36, p.35, 2016.

Y. Nesterov, « A Method of Solving a Convex Programming Problem with Convergence Rate O (1/K2), Soviet Mathematics Doklady. T. 27, p.19, 1983.

G. Neuhold, The Mapillary Vistas Dataset for Semantic Understanding of Street Scenes, Proceedings of the International Conference on Computer Vision (ICCV), p.34, 2017.

K. Nogueira, 2016 23rd International Conference on Pattern Recognition (ICPR). 2016 23rd International Conference on Pattern Recognition (ICPR). Déc, p.42, 2016.

H. Noh, S. Hong, and B. Han, « Learning Deconvolution Network for Semantic Segmentation, 2015 IEEE International Conference on Computer Vision (ICCV). 2015 IEEE International Conference on Computer Vision (ICCV). Déc, p.35, 2015.

A. Oliver, Realistic Evaluation of Semi-Supervised Learning Algorithms, Proceedings of the International Conference on Learning Representations Workshops (ICLR), p.20, 2018.

E. Oyallon, « Building a Regular Decision Boundary with Deep Networks, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.15, 2017.

S. Paisitkriangkrai, Effective Semantic Pixel Labelling with Convolutional Networks and Conditional Random Fields, p.43, 2015.

M. Pal, Random Forest Classifier for Remote Sensing Classification, vol.26, p.41, 2005.

M. Pal, M. Paul, and . Mather, Support Vector Machines for Classification in Remote Sensing, International Journal of Remote Sensing, vol.26, p.41, 2005.

M. Papadomanolaki, M. Vakalopoulou, and K. Karantzalos, « PatchBased Deep Learning Architectures for Sparse Annotated Very High Resolution Datasets, p.42, 2017.

C. P. Papageorgiou, M. Oren, and T. Poggio, « A General Framework for Object Detection, Proceedings of the Sixth International Conference on Computer Vision. ICCV '98, vol.939174, p.22, 1998.

S. Papert, The Summer Vision Project, vol.28, p.10, 1966.

P. Basa-pati and A. G. Ramakrishnan, « Word Level Multi-Script Identification, Pattern Recognition Letters, vol.29, pp.1218-1229, 2008.

C. Peng, Large Kernel Matters -Improve Semantic Segmentation by Global Convolutional Network, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR, pp.4353-4361, 2017.

M. T. Pham, E. Aptoula, and S. Lefèvre, « Feature Profiles from Attribute Filtering for Classification of Remote Sensing Images, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.11, issue.1, p.43, 2018.

N. Pinto, D. D. Cox, and J. J. Dicarlo, « Why Is Real-World Visual Object Recognition Hard ?, PLOS Computational Biology, vol.4, p.26, 2008.

T. Poggio, Why and When Can Deep-but Not Shallow-Networks Avoid the Curse of Dimensionality : A Review, vol.14, pp.1476-8186, 2017.

B. Polyak and A. Juditsky, Acceleration of Stochastic Approximation by Averaging, vol.30, p.19

. Ning-qian, On the Momentum Term in Gradient Descent Learning Algorithms, vol.12, pp.116-122, 1999.

R. Raina, A. Madhavan, and A. Y. Ng, « Large-Scale Deep Unsupervised Learning Using Graphics Processors, Proceedings of the 26th Annual International Conference on Machine Learning. ICML '09, p.12, 2009.

O. Ronneberger, P. Fischer, T. Brox, ;. «-u-net, and . De-nassir-navab, Sous la dir, Medical Image Computing and Computer-Assisted Intervention -MICCAI 2015 : 18th International Conference, vol.36, p.35, 2015.

F. Rosenblatt, The Perceptron : A Probabilistic Model for Information Storage and Organization In The Brain. 1957 (cf, vol.28, p.11

J. W. Rouse, « Monitoring Vegetation Systems in the Great Plains with ERTS, p.40, 1974.

D. E. Rumelhart, G. E. Hinton, and R. J. Williams, « Learning Internal Representations by Error Propagation, vol.17, p.16, 1986.

O. Russakovsky, 3 (11 avr, ImageNet Large Scale Visual Recognition Challenge, vol.115, p.28, 2015.

R. Salakhutdinov and G. Hinton, Deep Boltzmann Machines, vol.15, p.12, 2009.

S. Santurkar, How Does Batch Normalization Help Optimization ? (No, It Is Not About Internal Covariate Shift), p.27

A. M. Saxe, J. L. Mcclelland, and S. Ganguli, « Exact Solutions to the Nonlinear Dynamics of Learning in Deep Linear Neural Networks, Proceedings of the International Conference on Learning Representations (ICLR), vol.20, p.18, 2014.

I. Sutskever, On the Importance of Initialization and Momentum in Deep Learning, Proceedings of The 30th International Conference on Machine Learning, p.19, 2013.

C. Szegedy, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Juin, vol.32, p.35, 2015.

C. Szegedy, Rethinking the Inception Architecture for Computer Vision, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Juin, p.31, 2016.

C. Szegedy, Inception-v4, Inception-Resnet and the Impact of Residual Connections on Learning, AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence, p.33, 2017.

R. Szeliski, Computer Vision : Algorithms and Applications. Texts in Computer Science, p.28, 2011.

T. Tielman and G. Hinton, Lecture 6.5-RmsProp : Divide the Gradient by a Running Average of Its Recent Magnitude, p.19, 2012.

A. M. Turing, Computing Machinery and Intelligence. 1950 (cf, p.10

J. R. Uijlings, er sept, Selective Search for Object Recognition, vol.104, p.34, 2013.

S. Ullman, er août 1989), Aligning Pictorial Descriptions : An Approach to Object Recognition, vol.32, pp.193-254

D. Ulyanov, A. Vedaldi, and V. Lempitsky, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

M. Vakalopoulou, Building Detection in Very High Resolution Multispectral Data with Deep Learning Features, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Juil, p.42, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01264084

J. E. Vargas, Superpixel-Based Interactive Classification of Very High Resolution Images, 27th SIBGRAPI Conference on Graphics, Patterns and Images. Août, p.42, 2014.

A. Veit, M. Wilber, and S. Belongie, Proceedings of the 30th International Conference on Neural Information Processing Systems. NIPS'16, p.33, 2016.

M. Vidal-naquet and S. Ullman, Object Recognition with Informative Features and Linear Classification, p.28, 2003.

H. Zhao, Pyramid Scene Parsing Network, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Honolulu, United States, pp.2881-2890, 2017.

J. Zhao, Proceedings of the International Conference on Learning Representations (ICLR). 8 juin 2015, vol.35, p.25

S. Zheng, Proceedings of the IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision (ICCV). Déc, p.36, 2015.

B. Zhou, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.36, 2017.

Y. Zhou and R. Chellappa, ICASSP-88, International Conference on Acoustics, Speech, and Signal Processing, vol.2, p.26, 1988.

B. Zoph and Q. Le, Proceedings of the International Conference on Learning Representations (ICLR, p.16, 2017.

W. Zou, Generic Object Detection with Dense Neural Patterns and Regionlets, Proceedings of the British Machine Vision Conference, 2014.

R. Achanta, , p.62, 2010.

R. Achanta, SLIC Superpixels Compared to State-of-the-Art Superpixel Methods, vol.34, p.64, 2012.

N. Audebert and B. Le-saux-et-sébastien-lefèvre, « How Useful Is RegionBased Classification of Remote Sensing Images in a Deep Learning Framework ?, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016.

. Juil, , p.82, 2016.

N. Audebert and B. Le-saux-et-sébastien-lefèvre, « Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-Scale Deep Networks, Computer Vision -ACCV 2016, vol.82, p.81, 2016.

M. Baatz and A. Schäpe, « Multiresolution Segmentation : An Optimization Approach for High Quality Multi-Scale Image Segmentation, Angewandte Geographische Informationsverarbeitung XII : Beiträge zum AGIT-Symposium Salzburg, p.63, 2000.

V. Badrinarayanan, A. Kendall, R. Cipolla, and . Segnet, A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, p.68

Y. Bengio, A. Courville, and P. Vincent, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, p.66, 2013.

S. Beucher and F. Meyer, The Morphological Approach to Segmentation : The Watershed Transformation. Mathematical Morphology in Image Processing, vol.34, p.63, 1993.

P. Bosilj, Indexation et recherche d'images par arbres des coupes, p.61, 2016.
URL : https://hal.archives-ouvertes.fr/tel-01362165

L. Bottou, « Large-Scale Machine Learning with Stochastic Gradient Descent, COMPSTAT. 2010 (cf, p.67

A. Boulch, Office national d'études et de recherches aérospatiales, vol.81, p.80, 2015.

S. Brahimi, Multiscale Fully Convolutional DenseNet for Semantic Segmentation, WSCG 2018, International Conference on Computer Graphics, Visualization and Computer Vision, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01786688

G. Câmara, An Open Source GIS Library for Large-Scale Environmental and Socio-Economic Applications, Open Source Approaches in Spatial Data Handling. Advances in Geographic Information Science, p.74, 2008.

T. Chan and L. Vese, Scale-Space Theories in Computer Vision. International Conference on Scale-Space Theories in Computer Vision, p.63, 1999.

K. Chatfield, British Machine Vision Association, Proceedings of the British Machine Vision Conference. British Machine Vision Conference (BMVC), pp.6-7, 2014.

L. Chen, Semantic Image Segmentation with Deep Convolutional Nets, Atrous Convolution, and Fully Connected CRFs, vol.40, p.68

D. Comaniciu and P. Meer, Mean Shift : A Robust Approach toward Feature Space Analysis, vol.24, p.63, 2002.

C. Couprie, Toward real-time indoor semantic segmentation using depth information, Journal of Machine Learning Research, p.61, 2014.

J. Deng, A Large-Scale Hierarchical Image Database, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Juin, vol.68, p.66, 2009.

P. F. Felzenszwalb, P. Daniel, and . Huttenlocher, « Efficient Graph-Based Image Segmentation, International Journal of Computer Vision, vol.59, p.61

M. Gerke, Use of the Stair Vision Library within the ISPRS 2D Semantic Labeling Benchmark (Vaihingen), 270104226_Use_of_the_Stair_Vision_Library_ within _ the _ ISPRS _ 2D _ Semantic _ Labeling _ Benchmark _ (Vaihingen ) /links / 54ae59c50cf2828b29fcdf4b.pdf (cf, p.81, 2015.

R. Giraud, -. Vinh, N. Ta, and . Papadakis, Robust Superpixels Using Color and Contour Features along Linear Path, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01510063

M. Gong, Superpixel-Based Difference Representation Learning for Change Detection in Multispectral Remote Sensing Images, IEEE Transactions on Geoscience and Remote Sensing, vol.55, p.61, 2017.

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, p.66, 2016.

L. Grady, « Random Walks for Image Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.28, p.62, 2006.

T. Guyet, S. Malinowski, . Mohand-cherif, and . Benyounès, Extraction des zones cohérentes par l'analyse spatio-temporelle d'images de télédétection, Revue Internationale de Géomatique, vol.25, pp.1260-5875, 2015.

K. He, Proceedings of the IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision (ICCV). Déc, vol.77, p.68, 2015.

K. He, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, p.68, 2016.

Y. Jia, Proceedings of the 22Nd ACM International Conference on Multimedia. MM '14, vol.76, p.75, 2014.

J. Jiang, Residual Encoder-Decoder Network for Indoor RGB-D Semantic Segmentation, p.79

A. Lagrange, Benchmarking Classification of Earth-Observation Data : From Learning Explicit Features to Convolutional Networks, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Juil, p.66, 2015.

C. Lee, Deeply-Supervised Nets, Artificial Intelligence and Statistics. Artificial Intelligence and Statistics, vol.21, p.70, 2015.

A. Levinshtein, IEEE Transactions on Pattern Analysis and Machine Intelligence 31.12 (déc. 2009), p.63

H. Li, Superpixel-Based Feature for Aerial Image Scene Recognition, Sensors, vol.18, issue.1, p.61, 2018.

Z. Li and J. Chen, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), vol.74, p.62, 2015.

Z. Liao and G. Carneiro, Competitive Multi-Scale Convolution, p.70, 2015.

G. Lin, Efficient Piecewise Training of Deep Structured Models for Semantic Segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, pp.3194-3203, 2016.

G. Lin, Multi-Path Refinement Networks with Identity Mappings for High-Resolution Semantic Segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Juil. 2017, p.71

M. Liu, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). CVPR 2011. Juin, p.62, 2011.

Y. Liu, Dense Semantic Labeling of Very-High-Resolution Aerial Imagery and LiDAR with Fully-Convolutional Neural Networks and Higher-Order CRFs, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Honolulu, United States, p.81, 2017.

Y. Liu, Semantic Labeling in Very High Resolution Images via a SelfCascaded Convolutional Neural Network, 2017.

J. Long, E. Shelhamer, and T. Darrell, « Fully Convolutional Networks for Semantic Segmentation, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Juin, p.68, 2015.

D. Marmanis, Deep Learning Earth Observation Classification Using ImageNet Pretrained Networks, p.66

D. Marmanis, Semantic Segmentation of Aerial Images with an Ensemble of CNNs, ISPRS Annals of Photogrammetry, vol.3, p.80, 2016.

, Peer Neubert et Peter Protzel. « Compact Watershed and Preemptive SLIC : On Improving Trade-Offs of Superpixel Segmentation Algorithms. » Dans : ICPR, pp.62-64, 2014.

K. Nogueira, O. Penatti, A. Jefersson, and . Santos, « Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification, p.77, 2016.

S. Paisitkriangkrai, Effective Semantic Pixel Labelling with Convolutional Networks and Conditional Random Fields, vol.81, p.80, 2015.

F. Pedregosa, Machine Learning in Python, vol.12, p.75, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

O. Penatti, K. Nogueira, A. Jefersson, and . Santos, « Do Deep Features Generalize from Everyday Objects to Remote Sensing and Aerial Scenes Domains ?, Dans : Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, vol.77, p.66, 2015.

, PyTorch : Tensors and Dynamic Neural Networks in Python with Strong GPU Acceleration, p.76

. Nguyen-tien-quang, Proceedings of the Sixth International Symposium on Information and Communication Technology. International Symposium on Information and Communication Technology (SoICT), vol.81, p.80, 2015.

A. Sharif-razavian, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Juin, p.66, 2014.

J. Sherrah, « Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery, juin 2016), p.81

J. Shi and J. Malik, Normalized Cuts and Image Segmentation, vol.22, p.61, 2000.

K. Simonyan and A. Zisserman, « Very Deep Convolutional Networks for Large-Scale Image Recognition, Proceedings of the International Conference on Learning Representations (ICLR). Mai, p.68, 2015.

D. Stutz, A. Hermans-et-bastian-leibe, and . Superpixels, An Evaluation of the State-of-the-Art, Computer Vision and Image Understanding, vol.166, pp.1-27, 2018.

C. Szegedy, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Juin, vol.70, p.69, 2015.

J. C. Tilton, Best Merge Region-Growing Segmentation With Integrated Nonadjacent Region Object Aggregation, IEEE Transactions on Geoscience and Remote Sensing, vol.50, p.63, 2012.

M. Van-den and . Bergh, Sous la dir. d'Andrew Fitzgibbon et al. Lecture Notes in Computer Science 7578, SEEDS : Superpixels Extracted via Energy-Driven Sampling, pp.13-26, 2012.

. Stéfan-van-der-walt, Scikit-Image : Image Processing in Python, p.74, 2014.

A. Vedaldi and S. Soatto, Sous la dir. de David Forsyth, Philip Torr et Andrew Zisserman. Lecture Notes in Computer Science 5305, Quick Shift and Kernel Methods for Mode Seeking, p.63, 2008.

M. Volpi and D. Tuia, Dense Semantic Labeling of Subdecimeter Resolution Images With Convolutional Neural Networks, vol.55, p.81

J. Yosinski, How Transferable Are Features in Deep Neural Networks ? » Dans : Advances in Neural Information Processing Systems. Neural Information Processing Systems (NIPS), vol.77, p.66, 2014.

F. Yu-et-vladlen-koltun and . Multi, Scale Context Aggregation by Dilated Convolutions, Proceedings of the International Conference on Learning Representations (ICLR). 23 nov, vol.70, p.69, 2015.

W. Zhao and S. Du, Learning Multiscale and Deep Representations for Classifying Remotely Sensed Imagery, vol.70, p.69

. Références,

O. Arino, Global Land Cover Map for, vol.23, p.94, 2009.

N. Audebert and B. Le-saux-et-sébastien-lefèvre, « Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-Scale Deep Networks, Computer Vision -ACCV 2016, p.113, 2016.

N. Audebert and B. Le-saux-et-sébastien-lefèvre, « Beyond RGB : Very High Resolution Urban Remote Sensing with Multimodal Deep Networks, ISPRS Journal of Photogrammetry and Remote Sensing, p.113, 2017.

N. Audebert and B. Le-saux-et-sébastien-lefèvre, Deep Learning for Classification of Hyperspectral Data : A Comparative Review, p.113, 2019.
URL : https://hal.archives-ouvertes.fr/hal-02104998

A. Ben-hamida, Sous la dir. de SOILLE Pierre MARCHETTI Pier Giorgio, Deep Learning Approach for Remote Sensing Image Analysis, pp.106-108, 2016.

A. Ben-hamida, Deep Learning for Semantic Segmentation of Remote Sensing Images with Rich Spectral Content, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Juil. 2017, vol.112, p.94
URL : https://hal.archives-ouvertes.fr/hal-01654187

M. Bevilacqua and Y. Berthoumieu, Unsupervised Hyperspectral Band Selection via Multi-Feature Information-Maximization Clustering, 2017 IEEE International Conference on Image Processing (ICIP), 2017.
DOI : 10.1109/icip.2017.8296339

URL : https://hal.archives-ouvertes.fr/hal-01717011

G. Camps-valls, Composite Kernels for Hyperspectral Image Classification, IEEE Geoscience and Remote Sensing Letters, vol.3, issue.1, pp.93-97, 2006.

X. Ceamanos, Using 3D Information for Atmospheric Correction of Airborne Hyperspectral Images of Urban Areas, p.100, 2017.

P. S. Chavez, « Image-Based Atmospheric Corrections : Revisited and Improved, Photogrammetric engineering and remote sensing, vol.62, p.99, 1996.

Y. Chen, X. Zhao, and X. Jia, Spectral-Spatial Classification of Hyperspectral Data Based on Deep Belief Network, vol.8, p.105, 2015.

Y. Chen, Deep Learning-Based Classification of Hyperspectral Data, vol.7, p.105, 2014.

Y. Chen, Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks, vol.54, p.106, 2016.

Z. Chen, B. Gao, and B. Devereux, State-of-the-Art : DTM Generation Using Airborne LIDAR Data, vol.17, p.150, 2017.

M. Cubero-castan, A Physics-Based Unmixing Method to Estimate Subpixel Temperatures on Mixed Pixels, IEEE Transactions on Geoscience and Remote Sensing, vol.53, p.98
URL : https://hal.archives-ouvertes.fr/hal-01246504

Y. Cui, Laetitia Chapel et Sébastien Lefèvre. « Scalable Bag of Subpaths Kernel for Learning on Hierarchical Image Representations and Multi-Source Remote Sensing Data Classification, vol.9, p.196

F. Dell and &. Acqua, Exploiting Spectral and Spatial Information in Hyperspectral Urban Data with High Resolution, IEEE Geoscience and Remote Sensing Letters, vol.1, pp.322-326, 2004.

P. Y. Deschamps and T. Phulpin, Atmospheric Correction of Infrared Measurements of Sea Surface Temperature Using Channels at 3.7, 11 and 12 µm, BoundaryLayer Meteorology, vol.18, pp.6-8314, 1980.

S. Fabre, X. Briottet, and A. Lesaignoux, « Estimation of Soil Moisture Content from the Spectral Reflectance of Bare Soils in the 0.4-2.5 µm Domain, Sensors 15.2 (2 fév. 2015), pp.3262-3281

M. Fauvel and J. Chanussot-et-jón-atli-benediktsson, « A Spatial-Spectral Kernel-Based Approach for the Classification of Remote-Sensing Images, Pattern Recogn, vol.45, pp.381-392, 2012.

M. Fauvel, Advances in Spectral-Spatial Classification of Hyperspectral Images, vol.104, pp.652-675
URL : https://hal.archives-ouvertes.fr/hal-00737075

, Python Software Foundation. Python Language Reference

Q. Fu, Supervised Classification of Hyperspectral Imagery Based on Stacked Autoencoders, Proceedings of the 8th Interational Conference on Digital Image Processing (ICDIP). T. 10033, p.105, 2016.

B. Gao, Atmospheric Correction Algorithms for Hyperspectral Remote Sensing Data of Land and Ocean, Remote Sensing of Environment, vol.113, p.99, 2009.

P. Goel, Classification of Hyperspectral Data by Decision Trees and Artificial Neural Networks to Identify Weed Stress and Nitrogen Status of Corn, Computers and Electronics in Agriculture, vol.39, pp.168-1699, 2003.

K. He, Proceedings of the IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision (ICCV). Déc, p.110, 2015.

M. He, B. Li, and H. Chen, Multi-Scale 3D Deep Convolutional Neural Network for Hyperspectral Image Classification, p.106, 2017.

W. Hu, Deep Convolutional Neural Networks for Hyperspectral Image Classification, vol.105, p.107, 2015.
DOI : 10.1155/2015/258619

URL : http://downloads.hindawi.com/journals/js/2015/258619.pdf

A. Le-bris, Extraction of Optimal Spectral Bands Using Hierarchical Band Merging out of Hyperspectral Data, ISPRS -International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-3/W3, vol.20, pp.2194-9034
URL : https://hal.archives-ouvertes.fr/hal-01522444

Y. Lecun, Gradient-Based Learning Applied to Document Recognition, Proceedings of the IEEE, vol.86, p.106, 1998.

H. Lee and . Heesung-kwoon, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IGARSS. Beijing, juil, p.106, 2016.

T. Li, J. Zhang, and Y. Zhang, Classification of Hyperspectral Image Based on Deep Belief Networks, p.105, 2014.

Y. Li, H. Zhang, and Q. Shen, Spectral-Spatial Classification of Hyperspectral Imagery with 3D Convolutional Neural Network, vol.9, pp.106-108, 2017.

Z. Lin, Information, Communications and Signal Processing (ICICS) 2013 9th International Conference On. IEEE, p.105, 2013.

B. Liu, Supervised Convolutional Neural Network for Hyperspectral Image Classification, Remote Sensing Letters, vol.8, p.106

Y. Luo, A Novel Convolution Neural Network for Hyperspectral Image, p.106, 2018.

X. Ma, H. Wang, and J. Geng, « Spectral-Spatial Classification of Hyperspectral Image Based on Deep Auto-Encoder, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.9, issue.9, p.105

E. Mace, Overhead Detection : Beyond 8-Bits and RGB, août 2018), p.95

K. Makantasis, Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International. Geoscience and Remote Sensing Symposium (IGARSS), vol.106, p.105, 2015.

E. Midhun, Proceedings of the 2014 International Conference on Interdisciplinary Advances in Applied Computing. ICONIAAC '14, vol.35, p.105, 2014.

L. Mou, P. Ghamisi, and X. X. Zhu, Deep Recurrent Neural Networks for Hyperspectral Image Classification, vol.55, p.107

B. Ranga and . Myneni, The Interpretation of Spectral Vegetation Indexes, IEEE Transactions on Geoscience and Remote Sensing, vol.33, p.111, 1995.

V. Nair and G. E. Hinton, « Rectified Linear Units Improve Restricted Boltzmann Machines, Proceedings of the 27th International Conference on Machine Learning (ICML-10), p.107, 2010.

L. Parra, Unmixing Hyperspectral Data, Proceedings of the 12th International Conference on Neural Information Processing Systems, p.103, 1999.

F. Pedregosa, Machine Learning in Python, vol.12, p.107, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

A. Plaza, Spatial/Spectral Endmember Extraction by Multidimensional Morphological Operations, vol.40, pp.2025-2041

, PyTorch : Tensors and Dynamic Neural Networks in Python with Strong GPU Acceleration, vol.107, p.95

H. Rahman, G. «. Dedieu, and . Smac, A Simplified Method for the Atmospheric Correction of Satellite Measurements in the Solar Spectrum, International Journal of Remote Sensing, vol.15, pp.123-143, 1994.

F. Ratle, G. Camps-valls, and J. Weston, « Semisupervised Neural Networks for Efficient Hyperspectral Image Classification, IEEE Transactions on Geoscience and Remote Sensing, vol.48, p.105, 2010.

C. Rodarmel and J. Shan, « Principal Component Analysis for Hyperspectral Image Classification, Surveying and Land Information Science, vol.62, p.103, 2002.

A. Romero, C. Gatta, and G. Camps-valls, « Unsupervised Deep Feature Extraction for Remote Sensing Image Classification, IEEE Transactions on Geoscience and Remote Sensing PP, vol.99, p.106, 2015.

M. Sakamoto, Automatic Detection of Damaged Area of Iran Earthquake by High-Resolution Satellite Imagery, IEEE International Geoscience and Remote Sensing Symposium. T. 2, vol.2, p.111, 2004.

V. Slavkovikj, Hyperspectral Image Classification with Convolutional Neural Networks, p.105, 2015.

N. Srivastava, A Simple Way to Prevent Neural Networks from Overfitting, Journal of Machine Learning Research, vol.15, p.107, 2014.

C. Tao, Unsupervised Spectral-Spatial Feature Learning With Stacked Sparse Autoencoder for Hyperspectral Imagery Classification, IEEE Geoscience and Remote Sensing Letters, vol.12, p.105

Y. Tarabalka, J. Atli-benediktsson, and J. Chanussot, Spectral-Spatial Classification of Hyperspectral Imagery Based on Partitional Clustering Techniques, vol.47, pp.2973-2987, 2009.
URL : https://hal.archives-ouvertes.fr/hal-00449437

Y. Tarabalka, J. Chanussot, and J. A. Benediktsson, « Segmentation and Classification of Hyperspectral Images Using Watershed Transformation, Pattern Recognition, vol.43, pp.2367-2379, 2010.

;. Thierry-toutin, . Eros-a, and Q. Ikonos-ii, Comparison of Stereo-Extracted DTM from Different High-Resolution Sensors : SPOT-5, vol.42, p.110, 2004.

D. Tuia, Rémi Flamary et Nicolas Courty. « Multiclass Feature Learning for Hyperspectral Image Classification : Sparse and Hierarchical Solutions, pp.924-2716

D. Tuia, C. Persello, and L. Bruzzone, « Domain Adaptation for the Classification of Remote Sensing Data : An Overview of Recent Advances, IEEE Geoscience and Remote Sensing Magazine, vol.4, pp.2168-6831, 2016.

L. Wang, Spectral-Spatial Multi-Feature-Based Deep Learning for Hyperspectral Remote Sensing Image Classification, vol.21, pp.213-221, 2017.

J. Wu, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Juil, vol.104, pp.2614-2617, 2016.

C. Xing, L. Ma, and X. Yang, « Stacked Denoise Autoencoder Based Feature Extraction and Classification for Hyperspectral Images, Journal of Sensors, 2015.

Y. Wai-yeung, Ahmed Shaker et Nagwa El-Ashmawy. « Urban Land Cover Classification Using Airborne LiDAR Data : A Review, vol.158, pp.295-310, 2015.

Z. Yang, A Convolutional Neural Network-Based 3D Semantic Labeling Method for ALS Point Clouds, Remote Sensing, vol.9, p.936

J. Yue, Spectral-Spatial Classification of Hyperspectral Images Using Deep Convolutional Neural Networks, vol.6, p.106

W. Zhao and S. Du, Spectral-Spatial Feature Extraction for Hyperspectral Image Classification : A Dimension Reduction and Deep Learning Approach, vol.54, p.106, 2016.

W. Zhao, On Combining Multiscale Deep Learning Features for the Classification of Hyperspectral Remote Sensing Imagery, vol.36, p.106, 2015.

N. Audebert and B. Le-saux-et-sébastien-lefèvre, « Semantic Segmentation of Earth Observation Data Using Multimodal and Multi-Scale Deep Networks, Computer Vision -ACCV 2016, p.124, 2016.

N. Audebert and B. Le-saux-et-sébastien-lefèvre, « Beyond RGB : Very High Resolution Urban Remote Sensing with Multimodal Deep Networks, ISPRS Journal of Photogrammetry and Remote Sensing, p.134, 2017.

N. Audebert and B. Le-saux-et-sébastien-lefèvre, Joint Learning from Earth Observation and OpenStreetMap Data to Get Faster Better Semantic Maps, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Honolulu, United States, p.134, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01523573

V. Badrinarayanan, A. Kendall, R. Cipolla, and . Segnet, A Deep Convolutional Encoder-Decoder Architecture for Scene Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, p.121

T. Baltru?aitis, Chaitanya Ahuja et Louis-Philippe Morency. « Multimodal Machine Learning : A Survey and Taxonomy, vol.124, p.120, 2017.

J. Chen, A. Zipf, and . Deepvgi, Deep Learning with Volunteered Geographic Information, 26th International World Wide Web Conference

C. Couprie, Toward real-time indoor semantic segmentation using depth information, Journal of Machine Learning Research, 2014.

O. Danylo, Contributing to WUDAPT : A Local Climate Zone Classification of Two Cities in Ukraine, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol.9, issue.5, pp.1939-1404, 2016.

A. Eitel, 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol.121, p.120, 2015.

C. Geiß, Joint Use of Remote Sensing Data and Volunteered Geographic Information for Exposure Estimation : Evidence from Valparaíso, Natural Hazards, vol.86, p.131, 2017.

K. Noda, Audio-Visual Speech Recognition Using Deep Learning, vol.42, p.121
DOI : 10.1007/s10489-014-0629-7

URL : https://link.springer.com/content/pdf/10.1007%2Fs10489-014-0629-7.pdf

F. Ofli, 2013 IEEE Workshop on Applications of Computer Vision (WACV)

, , p.121, 2013.

F. Ordóñez and D. Roggen, Deep Convolutional and LSTM Recurrent Neural Networks for Multimodal Wearable Activity Recognition, vol.16, p.115, 2016.

S. Paisitkriangkrai, Effective Semantic Pixel Labelling with Convolutional Networks and Conditional Random Fields, p.122, 2015.

F. Ringeval, Introducing the RECOLA Multimodal Corpus of Remote Collaborative and Affective Interactions, 10th IEEE International Conference and Workshops on Automatic Face and Gesture Recognition (FG). Avr. 2013, p.121

B. Schuller, AVEC 2011-The First International Audio/Visual Emotion Challenge, p.121, 2011.

M. Schwarz, H. Schulz, and S. Behnke, « RGB-D Object Recognition and Pose Estimation Based on Pre-Trained Convolutional Neural Network Features, 2015 IEEE International Conference on Robotics and Automation (ICRA). Mai, p.121, 2015.

J. Sherrah, « Fully Convolutional Networks for Dense Semantic Labelling of High-Resolution Aerial Imagery, juin 2016), p.127

K. Simonyan and A. Zisserman, Two-Stream Convolutional Networks for Action Recognition in Videos, pp.568-576, 2014.

K. Simonyan and A. Zisserman, « Very Deep Convolutional Networks for Large-Scale Image Recognition, Proceedings of the International Conference on Learning Representations (ICLR). Mai, p.132, 2015.

X. Song, S. Jiang, and L. Herranz, Combining Models from Multiple Sources for RGB-D Scene Recognition, Proceedings of the 26th International Joint Conference on Artificial Intelligence. IJCAI'17, p.121, 2017.

N. Srivastava-et-ruslan and . Salakhutdinov, Multimodal Learning with Deep Boltzmann Machines, vol.15, p.121, 2014.

M. Vakalopoulou, Simultaneous Registration, Segmentation and Change Detection from Multisensor, Multitemporal Satellite Image Pairs. » Dans : International Geoscience and Remote Sensing Symposium (IGARSS). 10 juil, p.131, 2016.
DOI : 10.1109/igarss.2016.7729469

URL : https://hal.archives-ouvertes.fr/hal-01413373

C. Xie, Adversarial Examples for Semantic Segmentation and Object Detection, Proceedings of the IEEE International Conference on Computer Vision (ICCV), p.128, 2017.
DOI : 10.1109/iccv.2017.153

URL : http://arxiv.org/pdf/1703.08603

P. Ben, M. H. Yuhas, T. J. Goldstein, and . Sejnowski, « Integration of Acoustic and Visual Speech Signals Using Neural Networks, IEEE Communications Magazine, vol.27, p.121, 1989.

. Références,

J. Acquarelli, Convolutional Neural Networks and Data Augmentation for Spectral-Spatial Classification of Hyperspectral Images, p.142, 2017.

M. Arjovsky, Proceedings of the International Conference on Machine Learning (ICML). International Conference on Machine Learning. 17 juil, pp.214-223, 2017.

N. Audebert and B. Le-saux-et-sébastien-lefèvre, Generative Adversarial Networks for Realistic Synthesis of Hyperspectral Samples, p.153, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01809872

A. Barriuso and A. Torralba, Notes on Image Annotation, p.151, 2012.

A. Börner, SENSOR : A Tool for the Simulation of Hyperspectral Remote Sensing Systems, Journal of Photogrammetry and Remote Sensing, vol.55, pp.924-2716, 2001.

Y. Chen, Deep Feature Extraction and Classification of Hyperspectral Images Based on Convolutional Neural Networks, vol.54, p.142, 2016.

D. Amir-abbas, GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data, IEEE Geoscience and Remote Sensing Letters, vol.15, issue.6, p.142, 2018.

D. A. Van-dyk and X. Meng, The Art of Data Augmentation, p.142, 2012.

I. Gemp, Inverting Variational Autoencoders for Improved Generative Accuracy, NIPS Workshop on Advances in Approximate Bayesian Inference. 2017, vol.147, p.142

I. Goodfellow, Proceedings of the Neural Information Processing Systems (NIPS). NIPS, vol.143, p.142, 2014.

I. Gulrajani, Proceedings of the Neural Information Processing Systems (NIPS). NIPS, pp.5769-5779, 2017.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Proceedings of the Neural Information Processing Systems (NIPS). NIPS. 2012, pp.1097-1105

H. Lee and . Heesung-kwoon, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). IGARSS. Beijing, juil, p.142, 2016.

Z. Lu, Learning from Weak and Noisy Labels for Semantic Segmentation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, p.151

A. L. Maas, Y. Awni, A. Y. Hannun, and . Ng, ICML Workshop on Deep Learning for Audio, Speech and Language Processing, p.144, 2013.

L. Maggiolo, Improving Maps from CNNs Trained with Sparse, Scribbled Ground Truths Using Fully Connected CRFs, 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Juil, p.150, 2018.

K. Makantasis, Geoscience and Remote Sensing Symposium (IGARSS), 2015 IEEE International. Geoscience and Remote Sensing Symposium (IGARSS), p.142, 2015.

A. Odena, C. Olah, and J. Shlens, International Conference on Machine Learning. International Conference on Machine Learning. 17 juil, pp.2642-2651, 2017.

F. Pedregosa, Machine Learning in Python, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

T. Salimans, Proceedings of the Neural Information Processing Systems (NIPS). NIPS, p.145, 2016.

V. Slavkovikj, Hyperspectral Image Classification with Convolutional Neural Networks, pp.1159-1162, 2015.
DOI : 10.1145/2733373.2806306

URL : https://biblio.ugent.be/publication/7034491/file/7034499.pdf

T. Tielman and G. Hinton, Lecture 6.5-RmsProp : Divide the Gradient by a Running Average of Its Recent Magnitude, p.145, 2012.

L. Windrim, Hyperspectral CNN Classification with Limited Training Samples, p.142, 2016.

, Classification de données massives de télédétection Nicolas Audebert, 2018.

X. Chen, Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks, vol.11, p.160, 2014.

M. Cordts, Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, p.174, 2016.

N. Courty, Optimal Transport for Domain Adaptation, IEEE Transactions on Pattern Analysis and Machine Intelligence, p.170, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01170705

J. Dai, K. He, and J. Sun, « Instance-Aware Semantic Segmentation via Multi-Task Network Cascades, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, vol.171, p.166, 2016.

L. Eikvil, L. Aurdal, and H. Koren, « Classification-Based Vehicle Detection in High-Resolution Satellite Images, ISPRS Journal of Photogrammetry and Remote Sensing, vol.64, pp.65-72, 2009.

M. Everingham, The Pascal Visual Object Classes Challenge : A Retrospective, vol.111, p.164

J. Gleason, Vehicle Detection from Aerial Imagery, Robotics and Automation (ICRA), 2011 IEEE International Conference On. IEEE, pp.2065-2070, 2011.

Z. Hayder, X. He, and . Mathieu-salzmann, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), p.171, 2017.

C. Hazirbas, Incorporating Depth into Semantic Segmentation via Fusion-Based CNN Architecture, Computer Vision -ACCV 2016. Asian Conference on Computer Vision, vol.177, p.175, 2016.

K. He, Proceedings of the IEEE International Conference on Computer Vision. IEEE International Conference on Computer Vision (ICCV). Déc, p.175, 2015.

K. He, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas, vol.174, p.168, 2016.

K. He, Proceedings of the International Conference on Computer Vision. International Conference on Computer Vision (ICCV), vol.171, p.166, 2017.

A. C. Holt, Object-Based Detection and Classification of Vehicles from High-Resolution Aerial Photography, Photogrammetric Engineering & Remote Sensing, vol.75, p.160, 2009.

B. Huang, Scale Semantic Classification : Outcome of the First Year of Inria Aerial Image Labeling Benchmark, 2018 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 22 juil, vol.177, p.176, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01767807

P. Janney and D. Booth, Pose-Invariant Vehicle Identification in Aerial Electro-Optical Imagery, p.160

S. Jégou, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). Honolulu, United States, vol.177, p.175, 2017.

E. Jones, T. Oliphant, and P. Peterson, Open Source Scientific Tools for Python, 2001.

D. Kamenetsky and J. Sherrah, 2015 International Conference on Digital Image Computing : Techniques and Applications (DICTA). 2015 International Conference on Digital Image Computing : Techniques and Applications (DICTA), vol.166, p.160, 2015.

A. Kirillov, Panoptic Segmentation, p.179, 2018.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, Proceedings of the Neural Information Processing Systems (NIPS). NIPS. 2012, vol.167, p.161

. Tt and . Hoang-ngan-le, Reformulating Level Sets as Deep Recurrent Neural Network Approach to Semantic Segmentation, IEEE Transactions on Image Processing, vol.27, p.171, 2018.

F. Leberl, Recognizing Cars in Aerial Imagery to Improve Orthophotos, GIS : Proceedings of the ACM International Symposium on Advances in Geographic Information Systems. 1 er jan, p.160, 2007.

Y. Lecun, Gradient-Based Learning Applied to Document Recognition, Proceedings of the IEEE, vol.86, p.161, 1998.

Z. Liu, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.40, p.171, 2018.

E. Maggiori, Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing (IGARSS). IEEE International Symposium on Geoscience and Remote Sensing (IGARSS). 23 juil, vol.177, p.174, 2017.

D. Marmanis, « Classification With an Edge : Improving Semantic Image Segmentation with Boundary Detection, ISPRS Journal of Photogrammetry and Remote Sensing, p.160, 2017.

C. R. Maurer, R. Qi, and . Vijay-raghavan, « A Linear Time Algorithm for Computing Exact Euclidean Distance Transforms of Binary Images in Arbitrary Dimensions, IEEE Transactions on Pattern Analysis and Machine Intelligence 25.2 (fév. 2003), p.173

J. Michel, Local Feature Based Supervised Object Detection : Sampling, Learning and Detection Strategies, 2011 IEEE International Geoscience and Remote Sensing Symposium, vol.166, p.160, 2011.

K. Nogueira, O. Penatti, A. Jefersson, and . Santos, Towards Better Exploiting Convolutional Neural Networks for Remote Sensing Scene Classification, vol.167, p.161, 2016.

J. Ogier-du-terrail and F. Jurie, On the Use of Deep Neural Networks for the Detection of Small Vehicles in Ortho-Images, Proceedings of the International Conference on Image Processing (ICIP, p.160, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01527906

O. Penatti, K. Nogueira, A. Jefersson, and . Santos, « Do Deep Features Generalize from Everyday Objects to Remote Sensing and Aerial Scenes Domains ?, Dans : Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, p.167, 2015.

, PyTorch : Tensors and Dynamic Neural Networks in Python with Strong GPU Acceleration

X. Qi, « 3D Graph Neural Networks for RGBD Semantic Segmentation, Proceedings of the International Conference on Computer Vision. International Conference on Computer Vision (ICCV, vol.177, p.176, 2017.

H. Randrianarivo, B. L. Saux, and M. Ferecatu, 2013 IEEE International Geoscience and Remote Sensing Symposium -IGARSS. 2013 IEEE International Geoscience and Remote Sensing Symposium -IGARSS. Juil, vol.165, p.160, 2013.

H. Randrianarivo, Contextual Discriminatively Trained Model Mixture for Object Detection in Aerial Images, vol.164, p.160, 2016.

S. Razakarivony and F. Jurie, Vehicle Detection in Aerial Imagery : A Small Target Detection Benchmark, vol.34, p.160, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01122605

J. Redmon, You Only Look Once : Unified, Real-Time Object Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Juin, vol.165, p.160, 2016.

. Shaoqing-ren, Towards Real-Time Object Detection with Region Proposal Networks, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.39, issue.6, p.160, 2017.

F. Rottensteiner, The ISPRS Benchmark on Urban Object Classification and 3D Building Reconstruction, vol.1, p.3, 2012.

A. Lagrange, Benchmarking Classification of Earth-Observation Data : From Learning Explicit Features to Convolutional Networks, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Juil, pp.4173-4176, 2015.

. Bertrand-le-saux, IEEE GRSS Data Fusion Contest : Multimodal Land Use Classification, pp.2473-2397, 2018.

E. Maggiori, Proceedings of the IEEE International Symposium on Geoscience and Remote Sensing (IGARSS). IEEE International Symposium on Geoscience and Remote Sensing (IGARSS). 23 juil, 2017.

O. Penatti, K. Nogueira, A. Jefersson, and . Santos, « Do Deep Features Generalize from Everyday Objects to Remote Sensing and Aerial Scenes Domains ?, Dans : Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp.44-51, 2015.

H. Randrianarivo, B. L. Saux, and M. Ferecatu, 2013 IEEE International Geoscience and Remote Sensing Symposium -IGARSS. 2013 IEEE International Geoscience and Remote Sensing Symposium -IGARSS. Juil, pp.200-203, 2013.

S. Razakarivony and F. Jurie, Vehicle Detection in Aerial Imagery : A Small Target Detection Benchmark, vol.34, pp.187-203, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01122605

F. Rottensteiner, The ISPRS Benchmark on Urban Object Classification and 3D Building Reconstruction, vol.1, p.3, 2012.

S. Song, P. Samuel, J. Lichtenberg, . Xiao, and . Sun-rgb-d-:-a-rgb-d, Scene Understanding Benchmark Suite, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Juin, pp.567-576, 2015.

Y. Yang and S. Newsam, Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems. GIS '10, pp.270-279, 2010.

, GRSS Geoscience & Remote Sensing Society. III-V, vol.103, p.105

, Approximation discrète de la distribution locale du gradient dans une image selon une direction pré-définie, vol.12, p.161

. Hr-haute-résolution, Désigne une image de télédétection d'une résolution inférieure au sol à 1m, vol.42, p.66

. Hseg-hierarchial-segmentation, Algorithme de segmentation hiérarchique, vol.61, p.75

, IEEE Institute of Electrical and Electronics Engineers. III-V, vol.103, p.105

, IGN Institut national de l'information géographique et forestière, vol.3, p.181

. Irrv-image, , vol.II, p.182

. Irrvb-image, , vol.II, p.156

I. Vi and . Ix, ISPRS International Society for Photogrammetry and Remote Sensing. iv-viii, vol.63, pp.170-175

, Mesure de performance d'un classifieur utilisée notamment dans le cadre de la segmentation sémantique, Elle correspond au rapport du nombre d'échantillons étant positifs à la fois pour la classifieur et en réalité et du nombre d'échantillons étant positifs dans le classifieur ou dans la réalité, vol.71, pp.172-174

, JPEG Joint Photographic Experts Group, vol.22

, Lidar Light Detection And Ranging, technique de mesure de distance utilisant le temps parcouru par un faisceau lumineux entre son émission et la réception de son écho, vol.115, p.181

, LRN Local Response Normalization, vol.26, p.30

, Algorithme de segmentation superpixels, vol.60, pp.72-75

, Description de la topographie d'une surface terrestre prenant en compte les objets surélevés. Parfois également nommé modèle numérique de surface (MNS) dans la littérature, MNE Modèle Numérique d'Élévation, p.119

. Mnh-modèle-numérique-de-hauteur, Mesure de la hauteur des points surélevés par normalisation par rapport au terrain sous-jacent. v-vii, I-V, 39, 79, p.126

, Description de la topographie d'une surface terrestre, ne prenant pas en compte les objets surélevés. iv, 39, 105, vol.106, p.118

, Algorithme de segmentation multi-échelles implé-menté dans le logiciel eCognition, vol.72, p.73

, NDVI Normalized Difference Vegetation Index. 40, 99, 105, vol.107, p.118

, NDWI Normalized Difference Water Index, vol.40, p.99

. Nn-neural-network, , vol.104

, ONERA Office national d'études et de recherches aérospatiales

. Chapitre-c-liste and . Openstreetmap, , vol.182, pp.127-131

, PReLU Parametrized Rectified Linear Unit, vol.15

, RBM Restricted Boltzmann Machines, vol.12, p.101

, ReLU Rectified Linear Unit, vol.12, p.140

F. Rf-random, , vol.41, p.78

+. Rgb-d-red-green-blue, . Depth, . Vii, . Viii, and . Xix, , vol.77, pp.117-119

, RNN Recurrent Neural Network, vol.101, p.104

I. Xiii, RVB Espace de représentation des images naturelles sous forme de trois canaux rouge, vol.6, p.182

, SAR radar à synthèse d'ouverture (en anglais Synthetic Aperture Radar). XIX, 37, 39, 40, vol.61, p.181

, SCALP Superpixels with Contour Adherence using Linear Path. Algorithme de segmentation de type superpixels, p.60

, Algorithme de segmentation superpixels, SEEDS Superpixels Extracted via Energy-Driven Sampling, p.61

, Scale-Invariant Feature Transform) sont des caractéristiques images calculées sur des points d'intérêt cherchant une invariance à l'échelle, l'angle de vue et à la luminosité, SIFT Les descripteurs SIFT, vol.12, p.64

, SIG système d'information géographique. 39, 127, vol.130, p.162

, Algorithme de segmentation superpixels, SLIC Simple Linear Iterative Clustering, vol.72, p.73

, SPOT Satellites Pour l'Observation de la Terre, une famille de satellites de télédétection français conçus par le CNES. I, 2, 88, vol.147, p.181

, SVM Support Vector Machine, en français machine à vecteurs de support, parfois sous la dénomination Séparateur à Vaste Marge. Outil de classification ou de régression. vii, vol.12, p.161

, Désigne une image de télédétection d'une résolution infé-rieure au sol à 50cm. I, V, 42, THR Très haute résolution, vol.58, p.156

, AlexNet Architecture de réseau de neurones convolutif de classification d'images particulièrement populaire, arrivée en tête de la compétition ILSVRC en 2012, vol.29, p.162

, Caffe Bibliothèque logicielle C++ dotée d'interfaces Python et Matlab pour l'apprentissage profond, vol.74

, FuseNet Architecture de réseau de neurones entièrement convolutive multi-modale, dérivée de SegNet, pour la segmentation sémantique d'images RGB-D, vol.117, p.129

, hyperspectral Imagerie utilisant des récepteurs sur plusieurs dizaines de longueurs d'onde, y compris hors du domaine visible. IV, XIII, vol.38, p.181

, ImageNet Base de données contenant plus d'un million d'images annotées représentant mille classes d'objet, comportant aussi bien des voitures que des chats, des chiens, des chaises, des personnes, vol.12, p.147

, Landsat Premier programme spatial d'observation de la Terre, p.88

, multispectral Imagerie utilisant des récepteurs sur plusieurs longueurs d'onde. v, XIII, 37, 39, 61, vol.88, pp.91-94

, Pléiades Paire de satellites optiques très haute résolution français, vol.38, p.88

, PyTorch Bibliothèque logicielle C++/Python de calcul tensoriel sur CPU et GPU, spécia-lisée pour l'apprentissage profond, vol.74, p.103

, ResNet Architecture de réseau de neurones convolutif utilisant le paradigme d'apprentissage résiduel, vol.32, p.119

, scikit-learn Bibliothèque logicielle Python d'apprentissage automatique, p.103

, SegNet Architecture de réseau de neurones entièrement convolutive pour la segmentation sémantique. XIX, 66, 72, vol.88, p.171

, Sentinel Programme spatial d'observation de la Terre européen, démarré en 2014 avec les satellites SAR Sentinel-1A/B. La famille Sentinel comporte également les satellites multispectraux Sentinel-2A/B lancés en, vol.181, p.182, 2015.

, VGG-16 Architecture de réseau de neurones convolutif à 16 couches pour la classification d'images, vol.66, p.162