GPU acceleration of simplex volume algorithm for hyperspectral endmember extraction

Haicheng Qu; Junping Zhang; Zhouhan Lin; Hao Chen; Bormin Huang

doi:10.1117/12.977956

8 November 2012 GPU acceleration of simplex volume algorithm for hyperspectral endmember extraction

Haicheng Qu, Junping Zhang, Zhouhan Lin, Hao Chen, Bormin Huang

Proceedings Volume 8539, High-Performance Computing in Remote Sensing II; 85390B (2012) https://doi.org/10.1117/12.977956
Event: SPIE Remote Sensing, 2012, Edinburgh, United Kingdom

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.

Citation Download Citation

Haicheng Qu, Junping Zhang, Zhouhan Lin, Hao Chen, and Bormin Huang "GPU acceleration of simplex volume algorithm for hyperspectral endmember extraction", Proc. SPIE 8539, High-Performance Computing in Remote Sensing II, 85390B (8 November 2012); https://doi.org/10.1117/12.977956

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