high performance computing on graphics processing units: hgpu.org

Spectral unmixing amounts at automatically finding the signatures of pure spectral components (called endmembersin the hyperspectral imaging literature) and their associated abundance fractions in each pixel of the hyperspectral image. Many algorithms have been proposed to automatically find spectral endmembers in hyperspectral data sets. Perhaps one of the most popular ones is the pixel purity index (PPI), available in the ENVI software from Exelis Visual Information Solutions. This algorithm identifies the endmembers as the pixels with maxima projection values after projections onto a large randomly generated set of random vectors (called skewers). Although the algorithm has been widely used in the spectral unmixing community, it is highly time consuming as its precision increases asymptotically. Due to its high computational complexity, the PPI algorithm has been recently implemented in several high performance computing architectures including commodity clusters, heterogeneous and distributed systems, field programmable gate arrays (FPGAs) and graphics processing units (GPUs). In this letter, we present an improved GPU implementation of the PPI algorithm which provides real-time performance for the first time in the literature.