Parallel implementation of endmember extraction algorithms using NVidia graphical processing units
Department of Technology of Computers and Communications, Escuela Politecnica de Caceres, University of Extremadura, Avda. de la Universidad s/n, E-10071 Caceres, Spain
IEEE International Geoscience and Remote Sensing Symposium, 2009, IGARSS 2009
@inproceedings{plaza2009parallel,
title={Parallel implementation of endmember extraction algorithms using NVidia graphical processing units},
author={Plaza, A. and Plaza, J. and S{‘a}nchez, S.},
booktitle={Geoscience and Remote Sensing Symposium, 2009 IEEE International, IGARSS 2009},
volume={5},
pages={V–208},
organization={IEEE},
year={2009}
}
Spectral mixture analysis is an important task for remotely sensed hyperspectral data interpretation. In spectral unmixing, both the determination of spectrally pure signatures (endmembers) and the unmixing process that interprets mixed pixels as combinations of endmembers are computationally expensive procedures. An exciting recent development in the field of commodity computing is the emergence of programmable graphics processing units (GPUs), which are now increasingly being used address the ever-growing computational requirements introduced by hyperspectral imaging applications. In this paper, we develop three new GPU-based implementations of endmember extraction algorithms: the pixel purity index (PPI), a kernel version of the PPI (KPPI), and the automatic morphological endmember extraction (AMEE) algorithm. We also provide a GPU-based implementation of the fully constrained linear spectral unmixing algorithm.
July 17, 2011 by hgpu
