The team develops explorative algorithms to improve information retrieval from remote sensing data, in particular those from current and the next generation of Earth observation missions such as TerraSAR-X, TanDEM-X, TerraSAR-X follow-on, Tandem-L and EnMAP.
Currently, the team is working on the following main areas:
Sparse Earth Observation
Sparse signals are commonly expected in remote sensing. By exploiting the sparsity of signals, e.g. by using the Compressive Sensing theory, we can either achieve higher resolution compared to the Nyquist sampling theory or reduce the required number of samples with respect to a given resolution request. The team explores this idea for
For instance, for Tomographic SAR inversion, a super-resolution factor of up to 25 can be achieved. Fig.1 shows a point cloud from Tomographic SAR reconstruction of an area in Berlin using a TerraSAR-X high resolution spotlight image stack. For more examples, please go to our Sparse EO website .
Noise reduction is a standard step in EO data processing. Often classical local filters are used, e.g. multi-look-processing for SAR and InSAR data, which always reduce the spatial resolution. This calls for non-local approaches that take advantage of the high degree of redundancy of natural images.
The team explores the non-local concepts for
Fig. 2 compares a digital elevation model (DEM) of 6m ground spacing retrieved by using the non-local concept with the standard 12m TanDEM-X DEM.
Often remote sensing techniques suffer from unmodeled noise contributions and a large amount of outliers that make the employment of robust estimators important. Our team explores robust estimators for:
Figure 3 shows an example of the estimated linear deformation rate up to 20cm/year over an active volcanic area (Stromboli Volcano, Italy).
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, new sensor and mission concepts.