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Abundance Estimation Algorithms using NVIDIA CUDA Technology

David Gonzalez, Christian Sanchez, Ricardo Veguilla, Nayda G. Santiago, Samuel Rosario-Torres, Miguel Velez-Reyes
Center for Subsurface Sensing and Imaging Systems, Electrical and Computer Engineering Department, University of Puerto Rico, Mayaguez Campus
Proc. SPIE 6966, 69661E, 2008

@inproceedings{gonzalez2008abundance,

   title={Abundance estimation algorithms using NVIDIA CUDA technology},

   author={Gonz{‘a}lez, D. and S{‘a}nchez, C. and Veguilla, R. and Santiago, N.G. and Rosario-Torres, S. and V{‘e}lez-Reyes, M.},

   booktitle={Proceedings of SPIE},

   volume={6966},

   pages={69661E},

   year={2008}

}

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Spectral unmixing of hyperspectral images is a process by which the constituent’s members of a pixel scene are determined and the fraction of the abundance of the elements is estimated. Several algorithms have been developed in the past in order to obtain abundance estimation from hyperspectral data, however, most of them are characterized by being highly computational and time consuming due to the magnitude of the data involved. In this research we present the use of Graphic Processing Units (GPUs) as a computing platform in order to reduce computation time related to abundance estimation for hyperspectral images. Our implementation was developed in C using NVIDIA(R) Compute Unified Device Architecture (CUDATM). The recently introduced CUDA platform allows developers to directly use a GPU’s processing power to perform arbitrary mathematical computations. We describe our implementation of the Image Space Reconstruction Algorithm (ISRA) and Expectation Maximization Maximum Likelihood (EMML) algorithm for abundance estimation and present a performance comparison against implementations using C and Matlab. Results show that the CUDA technology produced results around 10 times better than the fastest implementation done on previous platforms.
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