So2Sat is an ambitious European Research Council (ERC) starting grand project. In the project, we will use revolutionary mapmaking methods to investigate how human settlements grow. We have the previlege to access the data supplied by several German and European earth observation satellites, which are equipped with innovative sensor technologies. We will develop new algorithms for the derivation of geo-information from these measurements. This makes it possible to create high-resolution 3D/4D maps of the cities up to individual building. For the first time, this information will also be combined with data from social networks: crowdsourcing platforms such as OpenStreetMap providing up-to-date map material; photos posted to the network providing authentic and current images in which buildings can be seen or which for example reveal the extent of damage caused by a flood. The major challenge here is consolidating this information and evaluating it automatically in a global scale.
The main subject of the scientific investigations within this project is the reconstruction of urban scenes by a stereogrammetric fusion of high-resolution spaceborne optical and SAR image data. The goal of this kind of sensor data fusion is to get a comprehensive three- dimensional description of urban topography. There are several reasons for this fusion: On the one hand, particularly spaceborne optical imagery are widely available and stored in great amounts in international Earth observation archives. On the other hand, also recent radar remote sensing missions, such as TerraSAR-X/TanDEM- X or CosmoSkymed, lead to a growing availability of high-resolution SAR data. Making use of the possibility to acquire new SAR data independently from daylight or weather conditions, this project wants to support the request to exploit existing archive data of regions of interest with greater flexibility but also to enable a timely mapping of critical areas by optimum combination of arbitrary satellite image data that is available on short notice. The results of this project will help to make the analysis of heterogeneous satellite image data as flexible as possible, in particular with respect to a rapid 3D mapping in time- critical applications.
The increased availability of data from different satellite and airborne sensors for a particular scene makes it desirable to jointly use data from multiple data sources for improved information extraction, hazard monitoring, and land cover/land use mapping. In this context, hyperspectral sensors provide detailed spectral information, which can be used to discriminate different classes of interest, but they do not provide structural and elevation information. On the other hand, LiDAR data can extract useful information related to the size, structure, and elevation of different objects, but cannot model the spectral characteristics of different materials. The main objective of this project goes to the proposition of efficient approaches for the integration of LiDAR and hyperspectral data.
The objective of the project is to develop a methodological framework for generalized coupled spectral unmixing to simultaneously unmix multisensor and multitemporal spectral images. The framework is applied to time series analysis and resolution enhancement of hyperspectral imagery. The outcome of the project will promote synergy and fusion of spaceborne hyperspectral and multispectral data (e.g., EnMAP and Sentinel-2).
Satellite remote sensing enables us to recover contact-free large-scale information about the physical properties of our Earth system from space. For information retrieval from these massive Earth observation data, efficient computing is necessary. To develop faster algorithms, especially for those large-scale problems that arise in Earth observation, it is thus inevitable to consider parallel computing. To this end, an interdisciplinary approach including optimal information retrieval and computationally efficient, parallelized solvers for large-scale problems seems the optimal solution, which is the focus of this project. Figure by courtesy of LRZ.
The objective of the project is to develop Compressive Sensing reconstruction algorithms for terahertz (THz) body scanners in order to improve the image quality of these scanners. Our project part focuses in particular on the development of joint 3D reconstruction techniques for FMCW THz radar imaging, that combine THz imaging in x,y and FMCW radar in the z direction. Using regularizers and exploiting the sparse properties of the signal during reconstruction, we attempt to improve quality and acquisition speed. Figure by courtesy of Sven Augustin.
SiPEO: 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.
This project makes use of the special configurations of the TanDEM-X Science Phase for precise 3D point localization and coastline detection. A joint feature of the investigated applications is the exploitation of large spatial and temporal baselines, which are available in Pursuit Monostatic Mode during the Science Phase. In this phase also the relatively new, high resolution Staring Spotlight Mode will be available for the first time in a single-pass interferometric configuration.
This project is an follow up of the project "4D City". It attempts the first reconstruction of objects from 3-D tomographic SAR point clouds, with the vision of building dynamic city models that could potentially be used to monitor and visualize the dynamics of urban infrastructure in very high level of details. The basic idea is to reconstruct 3-D building models via independent modeling of each individual façade to build the overall 2-D shape of the building footprint followed by its representation in 3-D.
This projects exploits the sparsity in remote sensing data, e.g. SAR signal in the elevation direction, and LiDAR full waveform. The project will identify sparse signals in remote sensing data, proide forward modeling and inversion techniques. Fast parallel sparse reconstruction solvers tailored to our problems will also be developed.
The research envisioned in this project leads to a new kind of city models for monitoring and visualization of the dynamics of urban infrastructure in a very high level of detail. The change or deformation of different parts of individual buildings will be accessible for different types of users (geologists, civil engineers, decision makers, etc.) to support city monitoring and management as well as risk assessment.