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Memorias de investigación
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Parallelism exploitation of a PCA algorithm for hyperspectral images using RVC-CAL
Research Areas
  • Electric engineers, electronic and automatic (eil)
Hyperspectral imaging (HI) collects information from across the electromagnetic spectrum, covering a wide range of wavelengths. The tremendous development of this technology within the field of remote sensing has led to new research fields, such as cancer automatic detection or precision agriculture, but has also increased the performance requirements of the applications. For instance, strong time constraints need to be respected, since many applications imply real-time responses. Achieving real-time is a challenge, as hyperspectral sensors generate high volumes of data to process. Thus, so as to achieve this requisite, first the initial image data needs to be reduced by discarding redundancies and keeping only useful information. Then, the intrinsic parallelism in a system specification must be explicitly highlighted. In this paper, the PCA (Principal Component Analysis) algorithm is implemented using the RVC-CAL dataflow language, which specifies a system as a set of blocks or actors and allows its parallelization by scheduling the blocks over different processing units. Two implementations of PCA for hyperspectral images have been compared when aiming at obtaining the first few principal components: first, the algorithm has been implemented using the Jacobi approach for obtaining the eigenvectors; thereafter, the NIPALS-PCA algorithm, which approximates the principal components iteratively, has also been studied. Both implementations have been compared in terms of accuracy and computation time; then, the parallelization of both models has also been analyzed. These comparisons show promising results in terms of computation time and parallelization: the performance of the NIPALS-PCA algorithm is clearly better when only the first principal component is achieved, while the partitioning of the algorithm execution over several cores shows an important speedup for the PCA-Jacobi. Thus, experimental results show the potential of RVC?CAL to automatically generate implementations which process in real-time the large volumes of information of hyperspectral sensors, as it provides advanced semantics for exploiting system parallelization. © (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
SPIE: High-Performance Computing in Geoscience and Remote Sensing VI
Edinburgh, United Kingdom
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SPIE Proceedings Vol. 10007: High-Performance Computing in Geoscience and Remote Sensing VI
  • Autor: Raquel Lazcano Lopez (UPM)
  • Autor: Ignacio Sidrach-Cardona
  • Autor: Daniel Madroñal Quintin (UPM)
  • Autor: K. Desnos (INSA-Rennes)
  • Autor: M. Pelcat (INSA-Rennes)
  • Autor: Eduardo Juarez Martinez (UPM)
  • Autor: Cesar Sanz Alvaro (UPM)
Research Group, Departaments and Institutes related
  • Creador: Grupo de Investigación: Grupo de Diseño Electrónico y Microelectrónico
  • Centro o Instituto I+D+i: Tecnologías del Software y Sistemas Multimedia para la Sostenibilidad (CITSEM)
  • Departamento: Ingeniería Telemática y Electrónica
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Cofinanciación del MINECO en el marco del Programa INNCIDE 2011 (OTR-2011-0236)
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