Difference between revisions of "Jobs/M2 TG 2020 DeepLearning Imagerie Hyperspectrale"

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(Created page with "{{Job |Reference id=M2 TG 2020 DeepLearning Imagerie Hyperspectrale |Title=Apprentissage de dynamique temporelle pour le démélange de séquences d’images hyperspectrales |...")
 
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[6] D. Nguyen, S. Ouala, L. Drumetz, and R. Fablet, “Em-like learning chaotic dynamics from noisy and partial observations,” arXiv preprint arXiv:1903.10335, 2019.
 
[6] D. Nguyen, S. Ouala, L. Drumetz, and R. Fablet, “Em-like learning chaotic dynamics from noisy and partial observations,” arXiv preprint arXiv:1903.10335, 2019.
 
[7] F. Rousseau, L. Drumetz, and R. Fablet, “Residual networks as flows of diffeomorphisms,” Journal of Mathematical Imaging and Vision, pp. 1–11, 2019.
 
[7] F. Rousseau, L. Drumetz, and R. Fablet, “Residual networks as flows of diffeomorphisms,” Journal of Mathematical Imaging and Vision, pp. 1–11, 2019.
|Contact=<guillaume . tochon at lrde . epita . fr>
+
|Contact=guillaume.tochon@lrde.epita.fr
 
|Compensation=1000 euros bruts/mois
 
|Compensation=1000 euros bruts/mois
 
|Type=Master Internship
 
|Type=Master Internship

Revision as of 15:35, 21 October 2019

Apprentissage de dynamique temporelle pour le démélange de séquences d’images hyperspectrales
Reference id

M2 TG 2020 DeepLearning Imagerie Hyperspectrale

Dates

5 - 6 mois

Research field

Traitement d'Images

Related project

Olena

Advisor

Guillaume Tochon

General presentation of the field
Prerequisites
Objectives

Tous les détails concernant le sujet de stage sont ici -> https://www.lrde.epita.fr/~gtochon/stage/fiche_poste_stage_apprentissage_resnet_teledetection.pdf

Benefit for the candidate
References

[1] J. M. Bioucas-Dias, A. Plaza, G. Camps-Valls, P. Scheunders, N. Nasrabadi, and J. Chanussot, “Hyperspectral remote sensing data analysis and future challenges,” IEEE Geoscience and remote sensing magazine, vol. 1, no. 2, pp. 6–36, 2013. [2] J. M. Bioucas-Dias, A. Plaza, N. Dobigeon, M. Parente, Q. Du, P. Gader, and J. Chanussot, “Hyperspectral unmixing overview: Geometrical, statistical, and sparse regression-based approaches,” IEEE journal of selected topics in applied earth observations and remote sensing, vol. 5, no. 2, pp. 354–379, 2012. [3] M. A. Goenaga, M. C. Torres-Madronero, M. Velez-Reyes, S. J. Van Bloem, and J. D. Chinea, “Unmixing analysis of a time series of hyperion images over the gu´anica dry forest in puerto rico,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 6, no. 2, pp. 329–338, 2012. [4] Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature, vol. 521, no. 7553, p. 436, 2015. [5] R. Fablet, S. Ouala, and C. Herzet, “Bilinear residual neural network for the identification and forecasting of dynamical systems,” arXiv preprint arXiv:1712.07003, 2017. [6] D. Nguyen, S. Ouala, L. Drumetz, and R. Fablet, “Em-like learning chaotic dynamics from noisy and partial observations,” arXiv preprint arXiv:1903.10335, 2019. [7] F. Rousseau, L. Drumetz, and R. Fablet, “Residual networks as flows of diffeomorphisms,” Journal of Mathematical Imaging and Vision, pp. 1–11, 2019.

Place LRDE: How to get to us
Compensation

1000 euros bruts/mois

Future work opportunities
Contact

guillaume.tochon@lrde.epita.fr