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http://hdl.handle.net/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: | http://hdl.handle.net/10553/42015 | ISSN: | 2072-4292 | DOI: | 10.3390/rs10060864 | Source: | Remote Sensing [ISSN 2072-4292], v. 10(6), 864 |
Appears in Collections: | Artículos |
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