A cooperation with the German Aerospace Center
"Earth observations include measurements and monitoring of the Earth under water, on the land surface and beneath, air and water quality, of atmospheric conditions, and measures of the health of humans, plants and animals" (GEOSS).
Among all measuring techniques, satellite remote sensing (RS) enables us to recover contact-free large-scale information about the physical properties of the Earth system from space. The importance of remote sensing can be reflected by the "Global Earth Observation System of Systems (GEOSS)" initiative in which more than 70 countries are involved and the "Copernicus" (former GMSE) program which is the second biggest European space program. A next generation of Earth observation (EO) missions is coming up. The most challenging ones are the radar missions (e.g. Sentinel-1 from ESA and TerraSAR-HD and Tandem-L from DLR) and hyperspectral missions (e.g. the German EnMap and NASA's HyspIRI satellite). Their novel features are extremely high spatial resolution, high spectral resolution or very high mapping capability and temporal sampling. These missions also employ new imaging modes. In order to best exploit this type of precious spaceborne - but also modern airborne - research infrastructure for a broad range of scientific or commercial applications, investigations on novel and sophisticated algorithms are important.
The Research Group Signal Processing in Earth Observation "SiPEO" develops explorative algorithms to improve information retrieval from remote sensing data, in particular those from current and the next generation of Earth observation missions. Currently, the team is working on the following main areas: 1) sparse Earth observation; 2) non-local filtering concept; 3) robust estimation. The improved retrieval of geo-information from EO data can be used to better support cartographic applications, resource management, civil security, disaster management, planning and decision making.
Global urbanization has lead to new challenges for urban planners aiming to reduce poverty and ensure sustainability. To overcome these challenges, we need sufficient data on a global scale.
We develop signal processing and machine learning algorithms to fuse Peta bytes of remote sensing data from different satellite missions with massive image data and text files from social networks.