@inproceedings{06b9fd0d8a1e475d84eb3070be4eab51,
title = "GPU acceleration of simplex volume algorithm for hyperspectral endmember extraction",
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.",
keywords = "CUDA, GPU, Simplex volume algorithm, endmember extraction",
author = "Haicheng Qu and Junping Zhang and Zhouhan Lin and Hao Chen and Bormin Huang",
year = "2012",
doi = "10.1117/12.977956",
language = "英语",
isbn = "9780819492791",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "High-Performance Computing in Remote Sensing II",
note = "High-Performance Computing in Remote Sensing II ; Conference date: 26-09-2012 Through 27-09-2012",
}