High-performance computing in remotely sensed hyperspectral imaging: the Pixel 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 a...

• Type: Conference
• Year: 2006
• Where: IPPS
• Authors: Antonio Plaza, David Valencia, Javier Plaza
• Keywords: Distributed And Parallel Computing, IPPS 2006, Remote Sensing, Remote Sensing Data, Remote Sensing Missions