Coupled Spectral Unmixing for Multidimensional High-Resolution Analysis
Xiaoxiang Zhu, Wotao Yin
Technical University of Munich
University of California, Los Angeles (UCLA)
Satellite remote sensing enables us to recover contact-free large-scale information about the physical properties of the Earth system from space. Its recent features are either extremely high spatial resolution, high spectral resolution or very high mapping capability and temporal sampling. For information retrieval from these massive amounts of Earth observation data, efficient computing is necessary. However, since 2005, the single-threaded CPU speed has stopped improving; it is the number of cores in each CPU and the number of CPUs in each workstation that continues to arise. 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. The project is granted by BaCaTec (High-Tech research between Bavaria and California). The following figure shows the supercomputing facility at Leibniz Supercomputing Centre (LRZ) where we have been granted access of over 20 million CPU hours.