|Title:||Parallel implementation of an iterative PCA algorithm for hyperspectral images on a manycore platform||Authors:||Lazcano, R.
Callico, G. M.
|UNESCO Clasification:||3307 Tecnología electrónica||Keywords:||Hyperspectral imaging
least squares approximations
geophysical image processing
|Issue Date:||2017||Project:||Hyperspectral Imaging Cancer Detection (Helicoid) (Contrato Nº 618080)||Journal:||Conference on Design and Architectures for Signal and Image Processing||Conference:||Conference on Design and Architectures for Signal and Image Processing (DASIP)||Abstract:||This paper presents a study of the par alle lization possibilities of a Non-Linear Iterative Partial Least Squares algorithm and its adaptation to a Massively Parallel Processor Array manycore architecture, which assembles 256 cores distributed over 16 clusters. The aim of this work is twofold: first, to test the behavior of iterative, complex algorithms in a manycore architecture; and, secondly, to achieve real-time processing of hyperspectral images, which is fixed by the image capture rate of the hyperspectral sensor. Real-time is a challenging objective, as hyperspectral images are composed of extensive volumes of spectral information. This issue is usually addressed by reducing the image size prior to the processing phase itself. Consequently, this paper proposes an analysis of the intrinsic parallelism of the algorithm and its subsequent implementation on a manycore architecture. As a result, an average speedup of 13 has been achieved when compared to the sequential version. Additionally, this implementation has been compared with other state-of-the-art applications, outperforming them in terms of performance.||URI:||http://hdl.handle.net/10553/46808||ISBN:||9781538635346||ISSN:||2164-9766||DOI:||10.1109/DASIP.2017.8122111||Source:||Conference on Design and Architectures for Signal and Image Processing, DASIP [ISSN 2164-9766], v. 2017-September, p. 1-6|
|Appears in Collections:||Actas de congresos|
checked on May 9, 2021
checked on May 10, 2021
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