The objective of the subject is to develop new methods, notably inspired by machine learning approach, for fusion of heterogenous data, hyper and multispectral images in particular. It takes place in the international James Webb Space Telescope (JWST) project, the most ambitious space telescope scheduled for launch in October 2021 and which will carry four instruments with unprecedented hyperspectral and multispectral observing capabilities.

Hyperspectral and multispectral imaging are ubiquitous in many observation modalities like Earth observations (like the European Copernicus project), astronomy, medical imaging, material analysis. Thus the work carried out during the PhD should also be applicable to many other modalities.

Details can be found here

https://pro.orieux.fr/files/data-fusion.pdf

• Keywords: Inverse problems, prior learning, heterogeneous data fusion, high dimensions, images.
• Tools: Optimization, Bayesian approach, machine learning.
• Applications: Hyperspectral imaging, astronomy.
• Supervision: F. Orieux (L2S - Univ. Paris-Saclay), A. Abergel (IAS - Univ. Paris-Saclay).
• Funding: The subject is fully funded, by CNES for 50% (application necessary before April 2) and the joint laboratory LAB4S2 for 50%.
• https://pro.orieux.fr orieux@l2s.centralesupelec.fr, (http://www.l2s.centralesupelec.fr) and alain.abergel@ias.u-psud.fr (IAS, Orsay)