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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.

The main objective of this paper is to describe a realistic framework to understand parallel performance of high-dimensional image processing algorithms in the context of heterogeneous networks of workstations (NOWs). As a case study, this paper explores techniques for mapping hyperspectral image analysis techniques onto fully heterogeneous NOWs. Hyperspectral imaging is a new technique in remote sensing that has gained tremendous popularity in many research areas, including satellite imaging and aerial reconnaissance. The automation of techniques able to transform massive amounts of hyperspectral data into scientific understanding in valid response times is critical for space-based Earth science and planetary exploration. Using an evaluation strategy which is based on comparing the efficiency achieved by an heterogeneous algorithm on a fully heterogeneous NOW with that evidenced by its homogeneous version on a homogeneous NOW with the same aggregate performance as the heterogeneous one, we develop a detailed analysis of parallel algorithms that integrate the spatial and spectral information in the image data through mathematical morphology concepts. For comparative purposes, performance data for the tested algorithms on Thunderhead (a large-scale Beowulf cluster at NASA's Goddard Space Flight Center) are also provided. Our detailed investigation of the parallel properties of the proposed morphological algorithms provides several intriguing findings that may help image analysts in selection of parallel techniques and strategies for specific applications.

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