Prof. Mihai Datcu
Prof. Mihai Datcu, German Aerospace Center DLR, Germany
Biography:
Mihai Datcu received the M.S. and Ph.D. degrees in electronics and telecommunications from the University Politehnica of Bucharest (UPB), Romania, in 1978 and 1986, respectively. In 1999, he received the title "Habilitation à diriger des recherches" in computer science from University Louis Pasteur, Strasbourg, France. Since 1981, he has been a Professor with the Faculty of Electronics, Telecommunications and Information Technology, UPB, working in signal/image processing and Electronic Speckle Interferometry. Since 1993, he has been a scientist with the German Aerospace Center (DLR), Oberpfaffenhofen, Germany. He is developing algorithms for analyzing Very High Resolution Synthetic Aperture Radar (VHR SAR) and Interferometric SAR (InSAR) data. He is engaged in research related to information theoretical aspects and semantic representations in advanced communication systems. Currently, he is Senior Scientist and Image Analysis research group leader with the Remote Sensing Technology Institute of DLR, Oberpfaffenhofen. Since 2011, he is also leading the Immersive Visual Information Mining research laboratory at the Munich Aerospace Faculty and is director of the Research Center for Spatial Information at UPB. He has held Visiting Professor appointments with the University of Oviedo, Spain, University Louis Pasteur and the International Space University, in Strasbourg, France, University of Siegen, Germany, University of Camerino, Italy, and the Swiss Center for Scientific Computing, Manno, Switzerland. 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, Telecom Paris, a collaboration of DLR with the French Space Agency (CNES). He has been Professor holder of the DLR-CNES Chair at ParisTech, Telecom Paris. His interests are in information and complexity theory, stochastic processes, Bayesian inference, and Image Information Mining (IIM). 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 is the author of more than 200 scientific publications, among them about 50 journal papers, and a book on number theory. He is a member of the European Image Information Mining Coordination Group (IIMCG) and of the Data Archiving and Distribution Technical Committee (DAD TC) of the IEEE Geoscience and Remote Sensing Society, and IEEE Fellow.
Title: Methods and Algorithms for Understanding of High Resolution SAR Images: Towards a SARINT Concept
Abstract:
Synthetic Aperture Radar (SAR) imagery, in the last two decades, has become increasingly popular as some of its properties are favorable and complementary to optical imagery. With the increase of the SAR sensor performance up to sub-meter resolution, a more detailed analysis and a finer description of SAR images over scenes, mainly object of human activities areas, are needed. The high diversity of man-made structures combined with the complexity of the scattering processes makes the analysis and information extraction, from high resolution SAR images over such areas, non-trivial. Since SAR provides N-dimensional complex valued signals, i.e. the information on the scene or target is modulating in amplitude, frequency or phase the radar echoes, the SAR data interpretation requires very specific techniques, to adapt to its “non-visual” nature. In addition the “sub-meter” resolution makes possible the detailed observation of man-made structures, thus the importance of SAR information is growing dramatically. In this frame is needed to develop expertise and tools for semi-automatic or automatic support to SAR data understanding and SAR Intelligence (SARINT).
The tutorial aims at presenting advanced methods for SAR image understanding based on interactive and automatic algorithms. The methods are intended for the analysis of the most used products: detected and single look complex images, and supported by coherent multiple observations in InSAR modes. The inclusion of model knowledge obtained from collections of pre-recorded physical target data leads to a comparison of the acquired data with representative models. Similarities and deviations revealed during the comparison allow a detailed high resolution interpretation of the image data and lead to a full image understanding. The lecture will give practical examples using TerraSAR-X images.
