Investigación

Publicación (Artículos en revistas científicas)

Adaptation of an Iterative PCA to a Manycore Architecture for Hyperspectral Image Processing

Lazcano López, Raquel; Madroñal Quintín, Daniel; Fabelo, Himar; Ortega, Samuel; Salvador, Rubén; Callicó, Gustavo M; Juárez Martínez, Eduardo; Sanz Álvaro, César
Resumen:
This paper presents a study of the adaptation of a Non-Linear Iterative Partial Least Squares (NIPALS) algorithm applied to Hyperspectral Imaging to a Massively Parallel Processor Array manycore architecture, which assembles 256 cores distributed over 16 clusters. This work aims at optimizing the internal communications of the platform to achieve real-time processing of large data volumes with limited computational resources and memory bandwidth. As hyperspectral images are composed of extensive volumes of spectral information, real-time requirements, which are upper-bounded by the image capture rate of the hyperspectral sensor, are a challenging objective. To address this issue, the image size is usually reduced prior to the processing phase, which is itself a computationally intensive task. Consequently, this paper proposes an analysis of the intrinsic parallelism and the data dependency within the NIPALS algorithm and its subsequent implementation on a manycore architecture. Furthermore, this implementation has been validated against three hyperspectral images extracted from both remote sensing and medical datasets. As a result, an average speedup of 17× has been achieved when compared to the sequential version. Finally, this approach has been compared with other state-of-the-art implementations, outperforming them in terms of performance.
Áreas de investigación:
Año:
2019
Tipo de publicación:
Artículos en revistas científicas
Palabras clave:
NIPALS-PCA; Hyperspectral imaging; Massively parallel processing; Real-time processing; Parallel programming
Revista:
Journal of Signal Processing Systems
Volumen:
91
Número:
7
Páginas:
759–771
Mes:
Mayo
ISSN:
1939-8115
DOI:
10.1007/s11265-018-1380-9
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