Talk by Mihai Datcu
(Centrale-Supélec, 3 rue Joliot-Curie, 91190 Gif-sur-Yvette: Salle du Conseil, Laboratoire des Signaux et Systèmes (L2S), Bâtiment Breguet, 11:00-12:30, May 4th, 2018; Café/croissants from 10:30)
The challenges of the Synthetic Aperture Radar (SAR) image formation principles, the high data volume and the very high acquisition rate stimulated from the very beginning the elaborations of sophisticated techniques. Meanwhile the SAR technologies have immensely evolved. The state of the art sensors deliver widely different imaging modes, and have made considerable progress in spatial and radiometric resolution, target acquisition strategies, or geographical coverage and data rates. Generally imaging sensors generate an isomorphic representation of the observed scene. This is not the case for SAR, the observations are a doppelganger of the scattered field, an indirect signature of the imaged object. This spots the load of SAR image understanding, and the outmost challenge of Big SAR Data Science, as a new and particular challenge of Machine Learning (ML) and Artificial Intelligence (AI). The presentation reviews and analyses the new approaches of SAR imaging leveraging the recent advances in physical process based ML and AI methods and signal processing. These is leading to Computational Imaging paradigms where intelligence is the analytical component of the end-to-end sensor and Data Science chain design. A particular focus is on the scientific methods of Deep Learning and an information theoretical model of the SAR information extraction process.