G. Mattyus, S. Wang, S. Fidler, and R. Urtasun, HD Maps: Fine-Grained Road Segmentation by Parsing Ground and Aerial Images, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
DOI : 10.1109/CVPR.2016.393

URL : http://elib.dlr.de/104583/1/Mattyus_CVPR16_compressed.pdf

D. Matthew, R. Zeiler, and . Fergus, Visualizing and understanding convolutional networks, 2014.

M. Volpi and D. Tuia, Dense semantic labeling of subdecimeter resolution images with convolutional neural networks, " arXiv preprint, 2016.

J. Sherrah, Fully convolutional networks for dense semantic labelling of high-resolution aerial imagery, 2016.

A. Dubrovina, P. Kisilev, B. Ginsburg, S. Hashoul, and R. Kimmel, Computational mammography using deep neural networks, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, vol.15, pp.1-5, 2016.
DOI : 10.1007/s11263-015-0816-y

H. Noh, S. Hong, and B. Han, Learning Deconvolution Network for Semantic Segmentation, 2015 IEEE International Conference on Computer Vision (ICCV), pp.1520-1528, 2015.
DOI : 10.1109/ICCV.2015.178

URL : http://arxiv.org/abs/1505.04366

V. Badrinarayanan, A. Handa, and R. Cipolla, Segnet: A deep convolutional encoder-decoder architecture for robust semantic pixel-wise labelling, 2015.

A. Paszke, A. Chaurasia, S. Kim, and E. Culurciello, Enet: A deep neural network architecture for real-time semantic segmentation, 2016.

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

D. Marmanisa, . Wegnera, . Gallianib, . Schindlerb, U. Datcuc et al., Semantic segmentation of aerial images with an ensemble of cnns, ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.473-480, 2016.

G. Liang-chieh-chen, I. Papandreou, K. Kokkinos, A. L. Murphy, and . Yuille, Semantic image segmentation with deep convolutional nets and fully connected crfs, 2014.

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

B. Hariharan, P. Arbeláez, R. Girshick, and J. Malik, Hypercolumns for object segmentation and fine-grained localization, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.447-456, 2015.
DOI : 10.1109/CVPR.2015.7298642

URL : http://arxiv.org/abs/1411.5752

M. Christopher and . Bishop, Neural networks for pattern recognition, 1995.

D. Clevert, T. Unterthiner, and S. Hochreiter, Fast and accurate deep network learning by exponential linear units (elus), 2015.

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

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.138.1115

Y. Boureau, J. Ponce, and Y. Lecun, A theoretical analysis of feature pooling in visual recognition, ICML, pp.111-118, 2010.

M. Kampffmeyer, A. Salberg, and R. Jenssen, Semantic Segmentation of Small Objects and Modeling of Uncertainty in Urban Remote Sensing Images Using Deep Convolutional Neural Networks, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2016.
DOI : 10.1109/CVPRW.2016.90

F. Yu and V. Koltun, Multi-scale context aggregation by dilated convolutions, 2015.

J. Yang, B. Price, S. Cohen, H. Lee, and M. Yang, Object contour detection with a fully convolutional encoderdecoder network, 2016.

V. Badrinarayanan, A. Kendall, and R. Cipolla, Segnet: A deep convolutional encoder-decoder architecture for image segmentation, 2015.

J. Tobias-springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller, Striving for simplicity: The all convolutional net, 2014.

E. Simo-serra, S. Iizuka, K. Sasaki, and H. Ishikawa, Learning to simplify, ACM Transactions on Graphics, vol.35, issue.4, p.121, 2016.
DOI : 10.1145/2897824.2925972

F. Pedro, . Felzenszwalb, B. Ross, D. Girshick, D. Mcallester et al., Object detection with discriminatively trained part-based models, IEEE Trans. Pattern Anal. Mach. Intell, vol.32, issue.9, pp.1627-1645, 2010.

C. Farabet, C. Couprie, L. Najman, and Y. Lecun, Learning Hierarchical Features for Scene Labeling, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1915-1929, 2013.
DOI : 10.1109/TPAMI.2012.231

URL : https://hal.archives-ouvertes.fr/hal-00742077

M. Gerke, Use of the stair vision library within the isprs 2d semantic labeling benchmark (vaihingen), Tech. Rep, 2015.

G. Russell, K. Congalton, and . Green, Assessing the accuracy of remotely sensed data: principles and practices, 2008.

S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, 2015.

K. He, X. Zhang, S. Ren, and J. Sun, Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification, 2015 IEEE International Conference on Computer Vision (ICCV), pp.1026-1034, 2015.
DOI : 10.1109/ICCV.2015.123

URL : http://arxiv.org/abs/1502.01852

K. Simonyan and A. Zisserman, Very deep convolutional networks for large-scale image recognition, 2014.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long et al., Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, 2014.
DOI : 10.1145/2647868.2654889