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Proceedings Article

Near real-time endmember extraction from remotely sensed hyperspectral data using NVidia GPUs

[+] Author Affiliations
Sergio Sánchez, Gabriel Martín, Abel Paz, Antonio Plaza, Javier Plaza

Univ. de Extremadura (Spain)

Proc. SPIE 7724, Real-Time Image and Video Processing 2010, 772409 (May 04, 2010); doi:10.1117/12.854365
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From Conference Volume 7724

  • Real-Time Image and Video Processing 2010
  • Nasser Kehtarnavaz; Matthias F. Carlsohn
  • Brussels, Belgium | April 12, 2010


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.

© (2010) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.

Sergio Sánchez ; Gabriel Martín ; Abel Paz ; Antonio Plaza and Javier Plaza
"Near real-time endmember extraction from remotely sensed hyperspectral data using NVidia GPUs", Proc. SPIE 7724, Real-Time Image and Video Processing 2010, 772409 (May 04, 2010); doi:10.1117/12.854365; http://dx.doi.org/10.1117/12.854365

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