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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Detalhes bibliográficos

Principais autores:	Ernestina Martel, Raquel Lazcano, José López, Daniel Madroñal, Rubén Salvador, Sebastián López, Eduardo Juarez, Raúl Guerra, César Sanz, Roberto Sarmiento
Formato:	Artigo
Idioma:	Inglês
Publicado em:	MDPI AG 2018-06-01
Colecção:	Remote Sensing
Assuntos:	hyperspectral imaging dimensionality reduction principal component analysis Jacobi method GPU manycore FPGA
Acesso em linha:	http://www.mdpi.com/2072-4292/10/6/864
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