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High-performance computing in remotely sensed hyperspectral imaging: the Pixel Purity Index algorithm as a case study

9 years 12 months ago
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...
Antonio Plaza, David Valencia, Javier Plaza
Added 12 Jun 2010
Updated 12 Jun 2010
Type Conference
Year 2006
Where IPPS
Authors Antonio Plaza, David Valencia, Javier Plaza
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