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One of the most important techniques for hyperspectral data exploitation is spectral unmixing, which aims at
characterizing mixed pixels. When the spatial resolution of the sensor is not fine enough to separate different
spectral constituents, these can jointly occupy a single pixel and the resulting spectral measurement will be a
composite of the individual pure spectra. The N-FINDR algorithm is one of the most widely used and successfully
applied methods for automatically determining endmembers (pure spectral signatures) in hyperspectral image
data without using a priori information. The identification of such pure signatures is highly beneficial in order
to 'unmix' the hyperspectral scene, i.e. to perform sub-pixel analysis by estimating the fractional abundance
of endmembers in mixed pixels collected by a hyperspectral imaging spectrometer. The N-FINDR algorithm
attempts to automatically find the simplex of maximum volume that can be inscribed within the hyperspectral
data set. Due to the intrinsic complexity of remotely sensed scenes and their ever-increasing spatial and spectral
resolution, the efficiency of the endmember searching process conducted by N-FINDR depends not only on the
size and dimensionality of the scene, but also on its complexity (directly related with the number of endmembers).
In this paper, we develop a new parallel version of N-FINDR which is shown to scale better as the dimensionality
and complexity of the hyperspectral scene to be processed increases. The parallel algorithm has been implemented
on two different parallel systems, in which two different types of commodity graphics processing units (GPUs)
from NVidia™ are used to assist the CPU as co-processors. Commodity computing in GPUs is an exciting
new development in remote sensing applications since these systems offer the possibility of (onboard) high
performance computing at very low cost. Our experimental results, obtained in the framework of a mineral
mapping application using hyperspectral data collected by the NASA Jet Propulsion Laboratory's Airborne
Visible Infra-Red Imaging Spectrometer (AVIRIS), reveal that the proposed parallel implementation compares
favorably with the original version of N-FINDR not only in terms of computation time, but also in terms of the
the accuracy of the solutions that it provides. The real-time processing capabilities of our GPU-based N-FINDR
algorithms and other GPU algorithms for endmember extraction are also discussed.
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