Abstract
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.
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