N. Audebert, L. Saux, B. Lefèvre, and S. , How useful is region-based classification of remote sensing images in a deep learning framework?, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2016.
DOI : 10.1109/IGARSS.2016.7730327

URL : https://hal.archives-ouvertes.fr/hal-01320016

V. Badrinarayanan, A. Kendall, and R. Cipolla, SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. arXiv preprint, 2015.

X. Chen, S. Xiang, C. L. Liu, and C. H. Pan, Vehicle Detection in Satellite Images by Hybrid Deep Convolutional Neural Networks, IEEE Geoscience and Remote Sensing Letters, vol.11, issue.10, pp.1797-1801, 2014.
DOI : 10.1109/LGRS.2014.2309695

M. Cramer, The DGPF test on digital aerial camera evaluation ? overview and test design, pp.73-82, 2010.

K. I. Winn, J. Zisserman, and A. , The Pascal Visual Object Classes Challenge: A Retrospective, International Journal of Computer Vision, vol.111, issue.1, pp.98-136, 2014.

S. Ioffe and C. Szegedy, Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, Proceedings of The 32nd International Conference on Machine Learning, pp.448-456, 2015.

A. Krizhevsky, I. Sutskever, and G. E. Hinton, ImageNet Classification with Deep Convolutional Neural Networks, Advances in Neural Information Processing Systems 25, pp.1097-1105, 2012.

A. Lagrange, L. Saux, B. Beaupere, A. Boulch, A. Chan-hon-tong et al., Benchmarking classification of earth-observation data: From learning explicit features to convolutional networks, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), pp.4173-4176, 2015.
DOI : 10.1109/IGARSS.2015.7326745

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2324, 1998.
DOI : 10.1109/5.726791

C. Liang-chieh, G. Papandreou, I. Kokkinos, K. Murphy, and A. Yuille, Semantic Image Segmentation with Deep Convolutional Nets and Fully Connected CRFs, Proceedings of the International Conference on Learning Representations, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01263610

T. Lin, M. Maire, S. Belongie, J. Hays, P. Perona et al., Microsoft COCO: Common Objects in Context, Computer Vision ? ECCV 2014, pp.740-755, 2014.
DOI : 10.1007/978-3-319-10602-1_48

J. Long, E. Shelhamer, and T. Darrell, Fully convolutional networks for semantic segmentation, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3431-3440, 2015.
DOI : 10.1109/CVPR.2015.7298965

D. Marmanis, J. D. Wegner, S. Galliani, K. Schindler, M. Datcu et al., Semantic Segmentation of Aerial Images with an Ensemble of CNNs, ISPRS Annals of Photogrammetry , Remote Sensing and Spatial Information Sciences, vol.3, pp.473-480, 2016.

K. Nogueira, O. A. Penatti, D. Santos, and J. A. , Towards better exploiting convolutional neural networks for remote sensing scene classification, Pattern Recognition, vol.61, 2016.
DOI : 10.1016/j.patcog.2016.07.001

S. Paisitkriangkrai, J. Sherrah, P. Janney, and A. V. 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

O. Penatti, K. Nogueira, D. Santos, and J. , Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?, 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.44-51, 2015.
DOI : 10.1109/CVPRW.2015.7301382

S. Razakarivony and F. Jurie, Vehicle detection in aerial imagery : A small target detection benchmark, Journal of Visual Communication and Image Representation, vol.34, pp.187-203, 2016.
DOI : 10.1016/j.jvcir.2015.11.002

URL : https://hal.archives-ouvertes.fr/hal-01122605

F. Rottensteiner, G. Sohn, J. Jung, M. Gerke, C. Baillard et al., THE ISPRS BENCHMARK ON URBAN OBJECT CLASSIFICATION AND 3D BUILDING RECONSTRUCTION, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.3, 2012.
DOI : 10.5194/isprsannals-I-3-293-2012

O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh et al., ImageNet Large Scale Visual Recognition Challenge, International Journal of Computer Vision, vol.1010, issue.1, pp.211-252, 2015.
DOI : 10.1007/s11263-015-0816-y

K. Simonyan and A. Zisserman, Very Deep Convolutional Networks for Large-Scale Image Recognition, 2014.

N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov, Dropout: A simple way to prevent neural networks from overfitting, J. Mach. Learn. Res, vol.15, issue.1, pp.1929-1958, 2014.