Exploitation de séries d'images SAR ou hyperspectrales

par Ammar Mian

Projet de thèse en Traitement du signal et des images

Sous la direction de Jean-Philippe Ovarlez.

Thèses en préparation à Paris Saclay , dans le cadre de Sciences et Technologies de l'Information et de la Communication , en partenariat avec SONDRA/SUPELEC (laboratoire) et de CentraleSupélec (établissement de préparation de la thèse) depuis le 01-10-2016 .


  • Résumé

    Remote sensing data with passive (multispectral, hyperspectral, etc.) or active (synthetic aperture radar, etc.) sensors offer a unique opportunity to record, to analyse and to predict the evolution of the earth, city, region of interest. In the last decade, many new satellite remote sensing missions (Sentinel-1, forthcoming Copernicus program) have been launched, resulting in dramatic improvement in the image acquisition capabilities with regular acquisition plans and free data access policy. This results in new challenge for handling and processing such huge volume of data. This increasing number of Earth Observation systems involves an enhanced possibility to acquire multi-temporal and multi-dimensional images of the Earth surface, with improved temporal and spatial resolution. Such new scenario significantly increases the interest of the time series processing. The development of novel data processing techniques to address new important and challenging applications is promising. The potential applications are abundant: change detection, infrastructure monitoring, long time sea or urban traffic surveillance, monitoring land-surface for motion risks, mapping for forest, water and soil management

  • Titre traduit

    Exploitation of Time Series of SAR or Hyperspectral Images


  • Résumé

    Remote sensing data with passive (multispectral, hyperspectral, etc.) or active (synthetic aperture radar, etc.) sensors offer a unique opportunity to record, to analyse and to predict the evolution of the earth, city, region of interest. In the last decade, many new satellite remote sensing missions (Sentinel-1, forthcoming Copernicus program) have been launched, resulting in dramatic improvement in the image acquisition capabilities. with regular acquisition plans and free data access policy. This results in new challenge for handling and processing such huge volume of data. This increasing number of Earth Observation systems involves an enhanced possibility to acquire multi-temporal and multi-dimensional images of the Earth surface, with improved temporal and spatial resolution. Such new scenario significantly increases the interest of the time series processing. The development of novel data processing techniques to address new important and challenging applications is promising. The potential applications are abundant: change detection, infrastructure monitoring, long time sea or urban traffic surveillance, monitoring land-surface for motion risks, mapping for forest, water and soil management