M. A. Aguilar, A. Fernández, F. J. Aguilar, F. Bianconi, and A. G. Lorca, Classification of urban areas from GeoEye-1 imagery through texture features based on Histograms of Equivalent Patterns, European Journal of Remote Sensing, vol.49, issue.1, pp.93-120, 2016.

A. Albert, J. Kaur, and M. C. Gonzalez, Using Convolutional Networks and Satellite Imagery to Identify Patterns in Urban Environments at a Large Scale, pp.1357-1366, 2017.

O. Aytekin, A. Erener, I. Ulusoy, and A. Duzgun, Unsupervised building detection in complex urban environments from multispectral satellite imagery, International Journal of Remote Sensing, vol.33, issue.7, pp.2152-2177, 2012.

R. Béranger, J. Blain, C. Baudinet, E. Faure, A. Flé-chon et al., Testicular germ cell tumours and early exposures to pesticides : The TESTEPERA pilot study, Bulletin Du Cancer, vol.101, issue.3, pp.225-235, 2014.

R. Béranger, O. Pérol, L. Bujan, E. Faure, J. Blain et al., Studying the impact of early life exposures to pesticides on the risk of testicular germ cell tumors during adulthood (TESTIS project) : study protocol, BMC Cancer, vol.14, issue.1, 2014.

G. Bradski, The OpenCV Library. Dr. Dobb's Journal of Software Tools, 2000.

S. Coveney and K. Roberts, Lightweight UAV digital elevation models and orthoimagery for environmental applications : data accuracy evaluation and potential for river flood risk modelling, International Journal of Remote Sensing, pp.1-22, 2017.

Z. Guo, L. Zhang, and D. Zhang, A Completed Modeling of Local Binary Pattern Operator for Texture Classification, IEEE Transactions on Image Processing, vol.19, issue.6, pp.1657-1663, 2010.

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.

M. Heikkila, M. Pietikainen, and C. Schmid, Description of interest regions with center-symmetric local binary patterns, 5th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP'06), vol.4338, pp.58-69, 2006.
URL : https://hal.archives-ouvertes.fr/inria-00548586

A. K. , I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Advances in Neural Information Processing Systems, vol.25, pp.1097-1105, 2012.

N. Kussul, M. Lavreniuk, A. Shelestov, and B. Yailymov, Along the season crop classification in Ukraine based on time series of optical and SAR images using ensemble of neural network classifiers, The International Geoscience and Remote Sensing Symposium, pp.7145-7148, 2016.

N. Kussul, M. Lavreniuk, S. Skakun, and A. Shelestov, Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data, IEEE Geoscience and Remote Sensing Letters, vol.14, issue.5, pp.778-782, 2017.

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

L. Liu, P. Fieguth, Y. Guo, X. Wang, and M. Pietikäi-nen, Local binary features for texture classification : Taxonomy and experimental study, Pattern Recognition, vol.62, pp.135-160, 2017.

L. Liu, S. Lao, P. W. Fieguth, Y. Guo, X. Wang et al., Median Robust Extended Local Binary Pattern for Texture Classification, IEEE Transactions on Image Processing, vol.25, issue.3, pp.1368-1381, 2016.

L. Liu, L. Zhao, Y. Long, G. Kuang, and P. Fieguth, Extended local binary patterns for texture classification. Image and Vision Computing, vol.30, pp.86-99, 2012.

T. Ojala, T. Maenpaa, M. Pietikainen, J. Viertola, J. Kyllonen et al., Outex -new framework for empirical evaluation of texture analysis algorithms, Proceedings -International Conference on Pattern Recognition, vol.1

T. Ojala, M. Pietikäinen, and T. Mäenpää, Gray Scale and Rotation Invariant Texture Classification with Local Binary Patterns, Computer Vision -ECCV 2000, vol.1842, pp.404-420, 2000.

T. Ojala, M. Pietikäinen, and T. Mäenpää, A Generalized Local Binary Pattern Operator for Multiresolution Gray Scale and Rotation Invariant Texture Classification, Advances in Pattern Recognition, pp.399-408, 2001.

F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion et al., Scikit-learn : Machine learning in Python, Journal of Machine Learning Research, vol.12, pp.2825-2830, 2011.
URL : https://hal.archives-ouvertes.fr/hal-00650905

A. Porebski, N. Vandenbroucke, L. Macaire, and D. Hamad, A new benchmark image test suite for evaluating colour texture classification schemes, Multimedia Tools and Applications, vol.70, pp.543-556, 2014.

S. Karen and Z. Andrew, Very deep convolutional networks for large-scale image recognition, 2014.

C. Silva, T. Bouwmans, and C. Frélicot, An extended center-symmetric local binary pattern for background modeling and subtraction in videos, International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISAPP 2015, 2015.
URL : https://hal.archives-ouvertes.fr/hal-01227955

X. Tan and B. Triggs, Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions, IEEE Transactions on Image Processing, vol.19, issue.6, pp.1635-1650, 2010.
URL : https://hal.archives-ouvertes.fr/inria-00548674

L. Wolf, T. Hassner, and Y. Taigman, Descriptor based methods in the wild, Workshop on faces in real-life images : Detection, alignment, and recognition, 2008.
URL : https://hal.archives-ouvertes.fr/inria-00326729

X. Wu, J. Sun, G. Fan, and Z. Wang, Improved Local Ternary Patterns for Automatic Target Recognition in Infrared Imagery, Sensors, vol.15, issue.12, pp.6399-6418, 2015.

C. Zhu, C. Bichot, and L. Chen, Image region description using orthogonal combination of local binary patterns enhanced with color information, Pattern Recognition, vol.46, issue.7, pp.1949-1963, 2013.
URL : https://hal.archives-ouvertes.fr/hal-01339149