Biograph: Mihai Datcu, received the MS and Ph.D. degrees in Electronics and Telecommunications from the University Politechnica Bucharest UPB, Romania, in 1978 and 1986. In 1999 he received the title Habilitationà diriger des recherché in Computer Science from University Louis Pasteur, Strasbourg, France. Since 1981 he is Professor with the Department of Applied Electronics and Information Engineering, Faculty of Electronics, Telecommunications and Information Technology (ETTI), UPB. Since 1993, he has been a scientist with the German Aerospace Center (DLR), Oberpfaffenhofen. Currently he is Senior Scientist and Image Mining research group leader with the Remote Sensing Technology Institute (IMF) of DLR, Oberpfaffenhofen. Since 2011 he is leading the Immersive Visual Information Mining research lab at the Munich Aerospace Faculty and he is director of the Research Center for Spatial Information at UPB. His interests are in Big Data Analytics, Data Science, Artificial Intelligence, Machine Learning and Computational Sensing. He has held Visiting Professor appointments with the University of Oviedo, Spain, the University Louis Pasteur and the International Space University, both in Strasbourg, France, University of Siegen, Germany, University of Innsbruck, Austria, University of Alcala, Spain, University Tor Vergata, Rome, Italy, Universidad Pontificia de Salamanca, campus de Madrid, Spain, University of Camerino, Italy, the Swiss Center for Scientific Computing (CSCS), Manno, Switzerland, China Academy of Science, Shenyang. From 1992 to 2002 he had a longer Invited Professor assignment with the Swiss Federal Institute of Technology, ETH Zurich. Since 2001 he has initiated and leaded the Competence Centre on Information Extraction and Image Understanding for Earth Observation, at ParisTech, Paris Institute of Technology, Telecom Paris, a collaboration of DLR with the French Space Agency (CNES). He has been Professor holder of the DLR-CNES Chair at ParisTech, Paris Institute of Technology, Telecom Paris. He initiated the European frame of projects for Image Information Mining (IIM) and is involved in research programs for information extraction, data mining and knowledge discovery and data understanding with the European Space Agency (ESA), NASA, and in a variety of national and European projects. He is a member of the ESA Big Data from Space Working Group. He and his team have developed and are currently developing the operational IIM processor in the Payload Ground Segment systems for the German missions TerraSAR-X, TanDEM-X, and the ESA Sentinel 1 and 2. He received in 2006 the Best Paper Award, IEEE Geoscience and Remote Sensing Society Prize, in 2008 the National Order of Merit with the rank of Knight, for outstanding international research results, awarded by the President of Romania, and in 1987 the Romanian Academy Prize Traian Vuia for the development of SAADI image analysis system and activity in image processing. He is representative of Romanian in the ESA Industrial Policy Committee (IPC) and Earth Observation Program Board (EO-PB). He is IEEE Fellow. In 2017 he was awarded the Chair Blaise Pascal for international recognition in the field of Data Science in Earth Observation, with the Centre d’Etudes et de Recherche en Informatique (CEDRIC) at the Conservatoire National des Arts et Métiers (CNAM) in Paris.
Title: Big SAR Data Science: Physics based Machine Learning and Artificial Intelligence
Abstract: Radar imaging, particularly Synthetic Aperture Radar (SAR) are pioneer technologies in the field of Computational Sensing and Imaging. The challenges of the image formation principles, high data volume and very high acquisition rate stimulated the elaborations of techniques witch today are ubiquitous. SAR technologies have immensely evolved, the state of the art sensors deliver widely different images, and have made considerable progress in spatial and radiometric resolution, target acquisition strategies, imaging modes, 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 positions the load of SAR image understanding, and the outmost challenge of Big SAR Data Science, as 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, and 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.