CNN-based segmentation and tracking of physical structures in satellite-derived sea surface image time series.
Supervisor: Ronan Fablet, Cédric Herzet (email@example.com)
Research team: IMT Atlantique, Lab-STICC, TOMS, Brest
Expected duration: 6 months
Contact person: firstname.lastname@example.org
Deep learning  has experienced tremendous growth in a few years in the field of artificial intelligence and computer vision. Initially exploited for classification and recognition problems, it has also become a reference framework for the resolution of signal and image processing problems: image synthesis, super-resolution, denoising, inpainting, segmentation...
The detection and tracking of physical structures (e.g., fronts, filaments, eddies...) in satellite-derived sea surface observations of the sea surface is a key issue for the characterization and understanding of the upper ocean dynamics [2,3,4]. Most approaches rely on rule-based algorithms [2,3] and only few studies have explored machine learning strategies . The goal of this internship will be to develop and evaluate deep learning models, especially CNN-based (Convolutional Neural Net) segmentation models [5,6,7], for these detection and tracking tasks. Two specific objectives will be of particular interest:
Case-studies for real satellite-derived observation datasets [e.g., 3] will be considered. All experiments will be implemented under Python using dedicated libraries such as Keras and/or Tensorflow frameworks.
Keywords: neural networks, CNN, objection detection and tracking, sea surface dynamics, physical structures, multi-source satellite observations
The envisioned workplan involves three main aspects:
 Y. LeCun, Y. Bengio, and G. Hinton. Deep learning. Nature, 521(7553):436– 444, May 2015.
 D. B. Chelton, M. G. Schlax, R. M. Samelson, and R. A. de Szoeke. Global observations of large oceanic eddies. Geophysical Research Letters, vol. 34, no. 15, 2007.
 E. Mason, A. Pascual, and J. C. McWilliams. A new sea surface height–based code for oceanic mesoscale eddy tracking. Journal of Atmospheric and Oceanic Technology, vol. 31, no. 5, pp. 1181–1188, 2014.
 M. D. Ashkezari, C. N. Hill, C. N. Follett, G. Forget, and M. J. Follows. Oceanic eddy detection and lifetime forecast using machine learning methods. Geophysical Research Letters, vol. 43, no. 23, 2016.
 Vijay Badrinarayanan, Alex Kendall and Roberto Cipolla "SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation. IEEE PAMI, 2017.
 N. Audebert, B. L. Saux, and S. Lefevre. Semantic segmentation of earth observation data using multimodal and multi-scale deep networks. arXiv preprint arXiv:1609.06846, 2016.
 O. Ronneberger, P. Fischer, and T. Brox. U-net: Convolutional networks for biomedical image segmentation. Int. Conference on Medical Image Computing and Computer-Assisted Intervention. Springer, 2015, pp. 234–241.
(c) GdR 720 ISIS - CNRS - 2011-2018.