X. Liu and Y. Bo, Object-Based Crop Species Classification Based on the Combination of Airborne Hyperspectral Images and LiDAR Data, Remote Sensing, vol.XII, issue.1, pp.922-950, 2015.
DOI : 10.1109/TGRS.2008.2005729

M. Alonzo, B. Bookhagen, and D. A. Roberts, Urban tree species mapping using hyperspectral and lidar data fusion, Remote Sensing of Environment, vol.148, pp.70-83, 2014.
DOI : 10.1016/j.rse.2014.03.018

M. Schmitt and X. X. Zhu, Data Fusion and Remote Sensing: An ever-growing relationship, IEEE Geoscience and Remote Sensing Magazine, vol.4, issue.4, pp.6-23, 2016.
DOI : 10.1109/MGRS.2016.2561021

Y. Lecun, Y. Bengio, and G. E. Hinton, Deep learning, Nature, vol.9, issue.7553, pp.436-444, 2015.
DOI : 10.1007/s10994-013-5335-x

C. Szegedy, W. Liu, Y. Jia, and P. Sermanet, Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-9, 2015.
DOI : 10.1109/CVPR.2015.7298594
URL : http://arxiv.org/pdf/1409.4842

T. H. Le, Y. Zheng, C. Zhu, K. Luu, and M. Savvides, Multiple Scale Faster-RCNN Approach to Driver???s Cell-Phone Usage and Hands on Steering Wheel Detection, 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.46-53, 2016.
DOI : 10.1109/CVPRW.2016.13

Y. Lecun, L. D. Jackel, L. Bottou, C. Cortes, J. Denker et al., Learning algorithms for classification: A comparison on handwritten digit recognition, Neural networks: the statistical mechanics perspective, p.276, 1995.

J. H. Bappy and A. K. Roy-chowdhury, CNN based region proposals for efficient object detection, 2016 IEEE International Conference on Image Processing (ICIP), pp.3658-3662, 2016.
DOI : 10.1109/ICIP.2016.7533042

A. Krizhevsk´ykrizhevsk´y, I. Sutskever, and G. E. Hinton, Imagenet classification with deep convolutional neural networks, Conference on Neural Information Processing Systems, pp.1097-1105, 2012.

V. Nair and G. E. Hinton, Rectified linear units improve restricted boltzmann machines, International Conference on Machine Learning, pp.807-814, 2010.

C. Garcia and M. Delakis, Convolutional face finder: a neural architecture for fast and robust face detection, International Conference on Learning Representations (ICLR), pp.1408-1423, 2004.
DOI : 10.1109/TPAMI.2004.97

M. Bertozzi, E. Binelli, A. Broggi, and M. Rose, Stereo Vision-based approaches for Pedestrian Detection, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05), Workshops, pp.16-16, 2005.
DOI : 10.1109/CVPR.2005.534
URL : http://www.cse.ohio-state.edu/OTCBVS/05/OTCBVS-05-FINALPAPERS/W01_22.pdf

J. Wagner, V. Fischer, M. Herman, and S. Behnke, Multispectral pedestrian detection using deep fusion convolutional neural networks, European Symp. on Artificial Neural Networks (ESANN), 2016.

G. Meyer, Machine vision identification of plants Recent Trends for Enhancing the Diversity and Quality of Soybean Products, Croatia: InTech, 2011.

A. Bannari, D. He, D. Morin, and H. Anys, Analyse de l'apport de deux indices de v??g??tation ?? la classification dans les milieux h??t??rog??nes, Canadian Journal of Remote Sensing, vol.17, issue.1, pp.233-239, 1998.
DOI : 10.1080/01431168208948387

M. Cramer, The DGPF-Test on Digital Airborne Camera Evaluation ??? Overview and Test Design, Photogrammetrie - Fernerkundung - Geoinformation, vol.2010, issue.2, pp.73-82, 2010.
DOI : 10.1127/1432-8364/2010/0041