Deep Learning for Synthetic Aperture Radar Remote Sensing - Tapa blanda

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Michael Schmitt has been a Full Professor for Earth Observation at the Department of Aerospace Engineering of the University of the Bundeswehr Munich (UniBw M) in Neubiberg, Germany, since 2021. From 2020 to 2022, he additionally held the position of a Consulting Senior Scientist at the Remote Sensing Technology Institute of the German Aerospace Center (DLR). Before joining UniBw M, he was a Professor for Applied Geodesy and Remote Sensing at the Munich University of Applied Sciences, Department of Geoinformatics. From 2015 to 2020, he was a Senior Researcher and Deputy Head at the Professorship for Signal Processing in Earth Observation at TUM; in 2019 he was additionally appointed as Adjunct Teaching Professor at the Department of Aerospace and Geodesy of TUM. In 2016, he was a guest scientist at the University of Massachusetts, Amherst. His research focuses on technical aspects of Earth observation, in particular image analysis and machine learning applied to the extraction of information from multi-modal remote sensing observations.

Ronny Hänsch is a scientist at the Microwave and Radar Institute of the German Aerospace Center (DLR) where he leads the Machine Learning Team in the Signal Processing Group of the SAR Technology Department. His research interest is computer vision and machine learning with a focus on remote sensing (in particular SAR processing and analysis). He was chair of the GRSS Image Analysis and Data Fusion (IADF) technical committee 2021-23, and serves as co-chair of the ISPRS working group on Image Orientation and Sensor Fusion, as editor in chief of the Geoscience and Remote Sensing Letters. associate editor the ISPRS Journal of Photogrammetry and Remote Sensing, and organizer of the CVPR Workshop EarthVision (2017-2024) and the IGARSS Tutorial on Machine Learning in Remote Sensing (2017-2024). He has extensive experience in organizing remote sensing community competitions (e.g. SpaceNet and the GRSS Data Fusion Contest).