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Proceedings Paper

GPU acceleration of simplex volume algorithm for hyperspectral endmember extraction
Author(s): Haicheng Qu; Junping Zhang; Zhouhan Lin; Hao Chen; Bormin Huang
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Paper Abstract

The simplex volume algorithm (SVA)1 is an endmember extraction algorithm based on the geometrical properties of a simplex in the feature space of hyperspectral image. By utilizing the relation between a simplex volume and its corresponding parallelohedron volume in the high-dimensional space, the algorithm extracts endmembers from the initial hyperspectral image directly without the need of dimension reduction. It thus avoids the drawback of the N-FINDER algorithm, which requires the dimension of the data to be reduced to one less than the number of the endmembers. In this paper, we take advantage of the large-scale parallelism of CUDA (Compute Unified Device Architecture) to accelerate the computation of SVA on the NVidia GeForce 560 GPU. The time for computing a simplex volume increases with the number of endmembers. Experimental results show that the proposed GPU-based SVA achieves a significant 112.56x speedup for extracting 16 endmembers, as compared to its CPU-based single-threaded counterpart.

Paper Details

Date Published: 8 November 2012
PDF: 7 pages
Proc. SPIE 8539, High-Performance Computing in Remote Sensing II, 85390B (8 November 2012); doi: 10.1117/12.977956
Show Author Affiliations
Haicheng Qu, Harbin Institute of Technology (China)
Liaoning Technical Univ. (China)
Junping Zhang, Harbin Institute of Technology (China)
Zhouhan Lin, Harbin Institute of Technology (China)
Hao Chen, Harbin Institute of Technology (China)
Bormin Huang, Univ. of Wisconsin-Madison (United States)


Published in SPIE Proceedings Vol. 8539:
High-Performance Computing in Remote Sensing II
Bormin Huang; Antonio J. Plaza, Editor(s)

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