. Boom, J. Bastiaan, . Huang, X. Phoenix, . Beyan et al., Long-term underwater camera surveillance for monitoring and analysis of fish populations, 2012.

F. , R. B. Chen-burger, G. Yun-heh, and . Daniela, Fish4Knowledge: Collecting and Analyzing Massive Coral Reef Fish Video Data, 2015.

A. , M. Khalil-sari, O. , K. Bin, and N. Azman, Fish recognition based on the combination between robust feature selection, image segmentation and geometrical parameter techniques using Artificial Neural Network and Decision Tree, Journal of Computer Science, vol.6, issue.10, pp.1088-1094

R. , A. Mori, G. , D. Lawrence, and M. , One Fish, Two Fish, Butterfish , Trumpeter: Recognizing Fish in Underwater Video, Machine Vision Applications, pp.404-407, 2007.

C. Spampinato, D. Giordano, D. Salvo, and . Roberto, Automatic fish classification for underwater species behavior understanding, Proceedings of the first ACM international workshop on Analysis and retrieval of tracked events and motion in imagery streams, ARTEMIS '10, pp.45-50, 2010.
DOI : 10.1145/1877868.1877881

H. , M. A. Dumais, S. T. Osman, and . Edgar, Support vector machines. Intelligent Systems and their Applications, pp.18-28, 1998.

M. , J. Kastner, R. Cutter-jr, and G. R. , Automated techniques for detection and recognition of fishes using computer vision algorithms In : NOAA Technical Memorandum NMFS-F/SPO-121, Report of the National Marine Fisheries Service Automated Image Processing Workshop, 2010.

S. , Y. , L. Sun-in, and C. Yi-hsuan, Fish observation, detection , recognition and verification in the real world, Proceedings of the International Conference on Image Processing, Computer Vision, and Pattern Recognition (IPCV), p.1, 2012.

K. Blanc, D. Lingrand, and F. Precioso, Fish Species Recognition from Video using SVM Classifier, Proceedings of the 3rd ACM International Workshop on Multimedia Analysis for Ecological Data, MAED '14, pp.1-6, 2014.
DOI : 10.1145/2661821.2661827
URL : https://hal.archives-ouvertes.fr/hal-01323227

Z. Qiang, Y. Mei-chen, and C. Kwang-ting, Fast Human Detection Using a Cascade of Histograms of Oriented Gradients, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Volume 2 (CVPR'06), pp.1491-1498, 2006.
DOI : 10.1109/CVPR.2006.119

D. and N. Triggs, Histograms of oriented gradients for human detection, Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, pp.886-893, 2005.
URL : https://hal.archives-ouvertes.fr/inria-00548512

P. , J. , C. Marc, S. , and G. , Comparaison de la segmentation pixel et segmentation objet pour la détection d'objets multiples et variables dans des images, CORESA: COmpression et REprésentation des Signaux Audiovisuels, 2014.

. Das, M. Sukhendu, T. T. Et, V. , and K. , Use of salient features for the design of a multistage framework to extract roads from highresolution multispectral satellite images. Geoscience and Remote Sensing, IEEE Transactions on, vol.49, issue.10, pp.3906-3931, 2011.

S. Xian, W. Hongqi, . Fu, and . Kun, Automatic detection of geospatial objects using taxonomic semantics. Geoscience and Remote Sensing Letters, pp.23-27, 2010.

. Zhang, . Wanceng, . Sun, . Xian, . Fu et al., Object detection in high-resolution remote sensing images using rotation invariant parts based model. Geoscience and Remote Sensing Letters, IEEE, vol.11, issue.1, pp.74-78, 2014.

. Zhang, . Wanceng, . Sun, W. Xian, and . Hongqi, A generic discriminative part-based model for geospatial object detection in optical remote sensing images, ISPRS Journal of Photogrammetry and Remote Sensing, vol.99, pp.30-44, 2015.
DOI : 10.1016/j.isprsjprs.2014.10.007

K. , A. Sutskever, H. Ilya, and G. E. , ImageNet classification with deep convolutional neural networks In : Advances in neural information processing systems, pp.1097-1105, 2012.

A. , P. M. Et, T. , and A. R. , Introduction neural networks in remote sensing, International Journal of remote sensing, vol.18, issue.4, pp.699-709, 1997.

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

C. Szegedy, . Liu, J. Wei, and . Yangqing, Going deeper with convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1-9, 2015.
DOI : 10.1109/CVPR.2015.7298594

J. , A. Goëau, . Hervé, . Glotin, and . Hervé, LifeCLEF 2015: multimedia life species identification challenges, Experimental IR Meets Multilinguality, Multimodality, and Interaction, pp.462-483, 2015.