|Title:||Adaptation of an Iterative PCA to a Manycore Architecture for Hyperspectral Image Processing||Authors:||Lazcano, R.
Callico, G. M.
|UNESCO Clasification:||3307 Tecnología electrónica||Keywords:||Hyperspectral imaging
Massively parallel processing
|Issue Date:||2018||Publisher:||1939-8018||Journal:||Journal of Signal Processing Systems||Abstract:||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.||URI:||http://hdl.handle.net/10553/46807||ISSN:||1939-8018||DOI:||10.1007/s11265-018-1380-9||Source:||Journal of Signal Processing Systems[ISSN 1939-8018], p. 1-13|
|Appears in Collections:||Artículos|
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