News

The department "EO Data Science" of Remote Senseng Technology Institue of DLR was founded on 01.04.2018 ! The following is a group photo of our colleagues.

We won the 2018 PRACE Ada Lovelace Award for HPC

Dear colleagues and friends, Parallel to my professorship at the Technical University of Munich (TUM) – Professor for signal processing in Earth Observation (TUM-SiPEO), which is a joint professorship with the German Aerospace Center (DLR), since April 1st, 2018, I am heading the fifth department…

Together with Jocelyn Chanussot, we chair the Whispers 2018 conference in Amsterdam, the Netherlands! 

Special Issue on “Computer Vision for Remote Sensing” Currently, massive streams of earth observation data are being systematically collected from different cutting-edge optical and radar sensors, on-board satellite, aerial and terrestrial platforms. These exponentially increasing amount of data…

Call for Papers: Remote Sensing Special Issue "Multisensor Data Fusion in Remote Sensing" You are welcomed to submit your relevant research outcome to the Remote Sensing special issue on “Multisensor Data Fusion in Remote Sensing” whose guest editors are Paul Scheunders , Xiaoxiang Zhu, and Naoto…

Call for Papers: JSTARS Special Issue You are welcomed to submit your relevant research outcome to the JSTARS special issue on “Recent Advances in Processing of High-Spatial-Resolution Remote Sensing Data” whose guest editors are Xin Huang, Xiaoxiang Zhu, Fabio Dell’Acqua, Mauro Dalla Mura, Mathieu…

Deep Recurrent Neural Networks for Hyperspectral Image Classification Our research paper "Deep Recurrent Neural Networks for Hyperspectral Image Classification" is the monthly most popular article of IEEE Transactions on Geoscience and Remote Sensing in July, August, and September 2017. Link to the…

Deep Learning in Remote Sensing: A Review Standing at the paradigm shift towards data-intensive science, as a major breakthrough in the field of machine learning, deep learning has proven as an extremely powerful tool in many fields. Shall we embrace deep learning as the key to all? Or, should we…