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Proceedings Paper

Hyperspectral image feature extraction accelerated by GPU
Author(s): HaiCheng Qu; Ye Zhang; Zhouhan Lin; Hao Chen
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Paper Abstract

PCA (principal components analysis) algorithm is the most basic method of dimension reduction for high-dimensional data1, which plays a significant role in hyperspectral data compression, decorrelation, denoising and feature extraction. With the development of imaging technology, the number of spectral bands in a hyperspectral image is getting larger and larger, and the data cube becomes bigger in these years. As a consequence, operation of dimension reduction is more and more time-consuming nowadays. Fortunately, GPU-based high-performance computing has opened up a novel approach for hyperspectral data processing6. This paper is concerning on the two main processes in hyperspectral image feature extraction: (1) calculation of transformation matrix; (2) transformation in spectrum dimension. These two processes belong to computationally intensive and data-intensive data processing respectively. Through the introduction of GPU parallel computing technology, an algorithm containing PCA transformation based on eigenvalue decomposition 8(EVD) and feature matching identification is implemented, which is aimed to explore the characteristics of the GPU parallel computing and the prospects of GPU application in hyperspectral image processing by analysing thread invoking and speedup of the algorithm. At last, the result of the experiment shows that the algorithm has reached a 12x speedup in total, in which some certain step reaches higher speedups up to 270 times.

Paper Details

Date Published: 24 October 2012
PDF: 9 pages
Proc. SPIE 8539, High-Performance Computing in Remote Sensing II, 85390M (24 October 2012); doi: 10.1117/12.974379
Show Author Affiliations
HaiCheng Qu, Harbin Institute of Technology (China)
Liaoning Technical Univ. (China)
Ye Zhang, Harbin Institute of Technology (China)
Zhouhan Lin, Harbin Institute of Technology (China)
Hao Chen, Harbin Institute of Technology (China)

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

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