Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons

Implementation of the Principal Component Analysis onto High-Performance Computer Facilities for Hyperspectral Dimensionality Reduction: Results and Comparisons

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 demandin...

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Bibliographic Details
Main Authors: Ernestina Martel, Raquel Lazcano, José López, Daniel Madroñal, Rubén Salvador, Sebastián López, Eduardo Juarez, Raúl Guerra, César Sanz, Roberto Sarmiento
Format: Article
Language:English
Published: MDPI AG 2018-06-01
Series:Remote Sensing
Subjects:
hyperspectral imaging
dimensionality reduction
principal component analysis
Jacobi method
GPU
manycore
FPGA
Online Access:http://www.mdpi.com/2072-4292/10/6/864

Internet

http://www.mdpi.com/2072-4292/10/6/864

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