Hyperspectral image feature extraction accelerated by GPU

HaiCheng Qu; Ye Zhang; Zhouhan Lin; Hao Chen

doi:10.1117/12.974379

24 October 2012 Hyperspectral image feature extraction accelerated by GPU

HaiCheng Qu, Ye Zhang, Zhouhan Lin, Hao Chen

Proceedings Volume 8539, High-Performance Computing in Remote Sensing II; 85390M (2012) https://doi.org/10.1117/12.974379
Event: SPIE Remote Sensing, 2012, Edinburgh, United Kingdom

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.

Citation Download Citation

HaiCheng Qu, Ye Zhang, Zhouhan Lin, and Hao Chen "Hyperspectral image feature extraction accelerated by GPU", Proc. SPIE 8539, High-Performance Computing in Remote Sensing II, 85390M (24 October 2012); https://doi.org/10.1117/12.974379

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