Title: Signal Processing and Information Retrieval in Remote Sensing
Speaker: Xiaoxiang Zhu (Professor for Signal Processing in Earth Observation, Technical University of Munich (TUM) & German Aerospace Center (DLR), Germany)
Abstract: Geoinformation derived from Earth observation satellite data is indispensable for many scientific, governmental and planning tasks. Cartography, Geophysics, resource management, civil security, disaster relief, as well as for planning and decision support are just a few examples. Therefore, the European Commission operates the Copernicus program that guarantees the future free access to remote sensing data delivered by Sentinels, a new fleet of ESA satellites. Germany also operates Earth observation satellites with so far the highest technical quality, including the current TerraSAR-X and TanDEM-X and the future EnMAP and Tandem-L missions.
Can modern mathematical signal processing algorithms improve information retrieval from remote sensing data, and hence take advantage of this precious satellite infrastructure more efficiently? In this talk, several modern signal processing concepts, including compressive sensing, nonlocal filters, non-negative matrix factorization, robust estimators and deep learning, are proposed for solving diverse scientific problems in remote sensing including radar and optical (multispectral and hyperspectral) technologies. The presented concepts are not only supposed to substantially improve information retrieval from existing sensors but also contribute to the preparation and the design of the next-generation Earth observation satellite missions.