Xiaoxiang Zhu


Title: Recent advances in very high resolution multi-dimensional SAR imaging (subject to changes)


Xiaoxiang Zhu is the professor for signal processing in earth observation at Technical University of Munich (TUM) and the German Aerospace Center (DLR). Zhu received the bachelor degree in space engineering from the National University of Defense Technology (NUDT), Changsha, China, in 2006. She received the Master (M.Sc.) degree, her doctor of engineering (Dr.-Ing.) degree and her “Habilitation” in the field of signal processing from TUM in 2008, 2011 and 2013, respectively. Prof. Zhu was a guest scientist or visiting professor at the Italian National Research Council (CNR-IREA), Naples, Italy, Fudan University, Shanghai, China, the University of Tokyo, Tokyo, Japan and University of California, Los Angeles, United States in 2009, 2014, 2015 and 2016, respectively.

Her main research interests are: advanced InSAR techniques such as high dimensional tomographic SAR imaging and SqueeSAR; computer vision in remote sensing including object reconstruction and multi-dimensional data visualization; big data analysis in remote sensing, and modern signal processing, including innovative algorithms such as sparse reconstruction, nonlocal means filter, robust estimation and deep learning, with applications in the field of remote sensing such as multi/hyperspectral image analysis.

Xiaoxiang Zhu is a senior member of IEEE, an associate editor of TGRS and the author of 200 scientific publications, among them more than 100 peer-reviewed papers and 10 paper awards. She has received several important scientific awards, for example the DFG Heinz Maier-Leibnitz-Preis in 2015, Innovators under 35, Technology Review Germany in 2015, IEEE GRSS early career award in 2016 and ERC Starting Grant in 2017 etc. Zhu is a member of young academy at the Berlin-Brandenburg Academy of Sciences and Humanities (BBAW) and the German National Academy of Sciences Leopoldina and the Bavarian Academy of Sciences and Humanities (BAdW), respectively.


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