High-performance computing in remotely sensed hyperspectral imaging: The purity index algorithm as a case study

The incorporation of last-generation sensors to airborne and satellite platforms is currently producing a nearly continual stream of high-dimensional data, and this explosion in the amount of collected information has rapidly created new processing challenges. For instance, hyperspectral imaging is a new technique in remote sensing that generates hundreds of spectral bands at different wavelength channels for the same area on the surface of the Earth. The price paid for such a wealth of spectral information available from latest-generation sensors is the enormous amounts of data that they generate. In recent years, several efforts have been directed towards the incorporation of high-performance computing (HPC) models in remote sensing missions. This paper explores three HPC-based paradigms for efficient information extraction from remote sensing data using the Pixel Purity Index (PPI) algorithm (available from the popular Kodak’s Research Systems ENVI software) as a case study for algorithm optimization. The three considered approaches are: 1) Commodity cluster-based parallel computing; 2) Distributed computing using heterogeneous networks of workstations; and 3) FPGA-based hardware implementations. Combined, these parts deliver an excellent snapshot of the state-of-the-art in those areas, and offer a thoughtful perspective on the potential and emerging challenges of adapting HPC models to remote sensing problems. 1