GPU implementation of the pixel purity index algorithm for hyperspectral image analysis
Hyperspectral imaging is a new technique in remote sensing that generates images with 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 is the enormous amounts of data to be processed. In recent years, several efforts have been directed towards the incorporation of high-performance computing models in remote sensing missions. For this purpose, graphics processing units (GPUs) have emerged as a very interesting type of hardware architecture in hyperspectral image processing due to its low weight and compact size, which allows for on-board data processing. In this paper, we develop an innovative GPU implementation of a standard hyperspectral image processing algorithm called pixel purity index (PPI) and utilized, among others, in commercial software tools such as ITTVIS Environment for Visualizing Images (ENVI) software originally developed by Analytical Imaging and Geophysics (AIG), one of the most popular tools currently available for processing remotely sensed data. The algorithm has been implemented using the compute device unified architecture (CUDA), and tested on the NVidia Tesla C1060 architecture, achieving a significant performance increase in the analysis of both synthetic and real hyperspectral data.