Please use this identifier to cite or link to this item: https://accedacris.ulpgc.es/handle/10553/42015
Title: Implementation of the principal component analysis onto high-performance computer facilities for hyperspectral dimensionality reduction: results and comparisons
Authors: Martel Jordán, Ernestina Ángeles 
Lazcano, Raquel
López Feliciano, José Francisco 
Madroñal, Daniel
Salvador, Rubén
López, Sebastián 
Juarez, Eduardo
Guerra, Raúl 
Sanz, César
Sarmiento, Roberto 
UNESCO Clasification: 330790 Microelectrónica
Keywords: Hyperspectral imaging
Dimensionality reduction
Principal component analysis
Jacobi method
GPU, et al
Issue Date: 2018
Publisher: 2072-4292
Journal: Remote Sensing 
Abstract: Dimensionality reduction represents a critical preprocessing step in order to increase the efficiency and the performance of many hyperspectral imaging algorithms. However, dimensionality reduction algorithms, such as the Principal Component Analysis (PCA), suffer from their computationally demanding nature, becoming advisable for their implementation onto high-performance computer architectures for applications under strict latency constraints. This work presents the implementation of the PCA algorithm onto two different high-performance devices, namely, an NVIDIA Graphics Processing Unit (GPU) and a Kalray manycore, uncovering a highly valuable set of tips and tricks in order to take full advantage of the inherent parallelism of these high-performance computing platforms, and hence, reducing the time that is required to process a given hyperspectral image. Moreover, the achieved results obtained with different hyperspectral images have been compared with the ones that were obtained with a field programmable gate array (FPGA)-based implementation of the PCA algorithm that has been recently published, providing, for the first time in the literature, a comprehensive analysis in order to highlight the pros and cons of each option.
URI: https://accedacris.ulpgc.es/handle/10553/42015
ISSN: 2072-4292
DOI: 10.3390/rs10060864
Source: Remote Sensing [ISSN 2072-4292], v. 10(6), 864
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