General scope of activities

Presentation

      The group is working on the developpement of image processing tools dedicated to SAR data and multi and hyper-spectral remote sensing images. The following research axes are developed :
      •  Speckle reduction and enhancement of SAR data (deep and self-supervised learning methods, non local approaches, dictionnary based approaches, TV regularization, markovian modeling and adapted optimization tools)
      •  Multi-temporal analysis (deep learning methods, non local approaches, statistical tests, local descriptors)
      • Understanding and information extraction (statistical modeling, phase vortex, graphs of primitives, stable points and corner reflectors, deep learning methods)
      • 3D reconstruction (interferometric and tomographic approaches, multi-view reconstruction)
      • Fusion of SAR and optical data (deep-learning, statistical methods)
      • Multi-spectral and hyper-spectral imaging (super-resolution, unmixing, classification)



      Scientific projects

      Ongoing / previous projects :
      • ASTER ANR ASTRID project for the development of deep and self-supervised learning methods dedicated to high resolution SAR imaging and applications (2027-2030)
      • CIEDS ALIA project for the development of deep learning methods dedicated remote sensing applications (2023-2027)
      • ASTRAL ANR ASTRID project for the development of deep learning methods dedicated to SAR imaging and applications (2022-2026)
      • COSMIC project (PHC Aurora) Advanced processing of SAR images for the Arctic (2021-2023) in collaboration with the Arctic University of Norway
      • Futur & Rupture funding on deep learning methods for forest monitoring using tomographic data (2021-2024)
      • Futur & Rupture funding on deep learning methods for remote sensing image understanding (2020-2023)
      • Exploiting spectral information or SAR time series processing (CNES, 2020-2022) -with MAP5-
      • Multi-temp-BIOMASS project for multitemporal speckle reduction for coming BIOMASS mission (CNES, 2017-2019)
      • SWOT-ADT for water surface detection for coming SWOT mission (CNES, 2018-2022)
      • ALYS ANR ASTRID DGA project for SAR tomography in urban areas (2016-2020) ALYS
      • MIRIAM ANR project for multi-images exploitation (2014-2018)
      • CDS funding (Center for Data Science), funded by the IDEX Paris-Saclay, ANR-11-IDEX-0003-02 (2015)
      • G4M project for underground network detection using GPR (Ground Penetrating Radar (2013-2017)
      • Futur & Rupture and CNES project on surface water and hydrological network extraction with KaRIn SWOT data (2014-2017)
      • CEA/DGA project on multi-sensor SAR registration and analysis (2011-2015)
      • PEPS project BAYARAS on asymetric Bayesian estimators for SAR image analysis (PI M. Sigelle, with J. Darbon) (2013-2014)
      • SWOT on line detection for hydrological network extraction (2010-2013)
      • DGA-REI project on SAR imagery regularization (2009-2011)
      • ANR EFIDIR on ground movement monitoring (2007-2011)
      • CMCU project on multi-temporal SAR image analysis (2008-2011)



      Collaborations


      Academic collaborations:
      • Laboratoire Hubert Curien, Telecom Saint-Etienne (Loic denis)
      • CESBIO (L. Ferro-Famil)
      • Arctic University of Norway (A. Marinoni)
      • Universität der Bundeswehr, Deutschland (M. Schmitt, F. Sica)
      • MAP5, Paris 5 (Rémy Abergel, Andres Almansa)
      • CEA (Beatrice Puyssegur)
      • SupCom Tunis (Ferdaous Chaabane)
      • University of Naples (Parthenope) (Giampaolo Ferraioli, Vito Pascazio)
      • University of Sao Paulo, Brazil, (Roberto Cesar, Roberto Hirata)

      Industrial or state partners:
      • CNES
      • ONERA
      • Magellium
      • Thales
      • C-S