PHYSIS

Sparse Signal Processing Technologies For HyperSpectral Systems
 

The objective of PHySIS is to develop, test, and evaluate novel signal processing technologies for real-time processing of hyperspectral data cubes. Although hyperspectral sensors capture massive amounts of high-dimensional data, relevant information usually lies in a low-dimensional space. Our aim is to extend recent theoretical and algorithmic developments in the field of sparsity-enforcing recovery, compressive sensing, and matrix completion, in order to build and exploit sparse representations adapted to the hyperspectral signals of interest. It is envisaged that all three, temporal, spatial and spectral domains of hyperspectral data will be explored for sparse representations. Thus, sparsity in the data will be used not only to improve estimation performance, but also to mitigate the enormous computational burden needed to analyze hyperspectral data and leverage the development of real-time hyperspectral processing systems.

PHySIS is a 24 months project started in March 2015 focusing on Bottom-up space technologies at low TRL and is funded by the European Commission under the H2020-COMPET-06-2014.

Project web site HERE.