The simplex volume algorithm (SVA)<sup>1</sup> 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.
Proc. SPIE. 8539, High-Performance Computing in Remote Sensing II
KEYWORDS: Signal to noise ratio, Hyperspectral imaging, Principal component analysis, Data compression, Image processing, Denoising, Feature extraction, Data processing, Parallel computing, Dimension reduction
PCA (principal components analysis) algorithm is the most basic method of dimension reduction for high-dimensional
data<sup>1</sup>, 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 processing<sup>6</sup>. 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 <sup>8</sup>(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.