Share Email Print

Proceedings Paper

Parallel implementation of a hyperspectral image linear SVM classifier using RVC-CAL
Author(s): D. Madroñal ; H. Fabelo; R. Lazcano; G. M. Callicó; E. Juárez; C. Sanz
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper Abstract

Hyperspectral Imaging (HI) collects high resolution spectral information consisting of hundreds of bands across the electromagnetic spectrum –from the ultraviolet to the infrared range–. Thanks to this huge amount of information, an identification of the different elements that compound the hyperspectral image is feasible. Initially, HI was developed for remote sensing applications and, nowadays, its use has been spread to research fields such as security and medicine. In all of them, new applications that demand the specific requirement of real-time processing have appear. In order to fulfill this requirement, the intrinsic parallelism of the algorithms needs to be explicitly exploited.

In this paper, a Support Vector Machine (SVM) classifier with a linear kernel has been implemented using a dataflow language called RVC-CAL. Specifically, RVC-CAL allows the scheduling of functional actors onto the target platform cores. Once the parallelism of the classifier has been extracted, a comparison of the SVM classifier implementation using LibSVM –a specific library for SVM applications– and RVC-CAL has been performed.

The speedup results obtained for the image classifier depends on the number of blocks in which the image is divided; concretely, when 3 image blocks are processed in parallel, an average speed up above 2.50, with regard to the RVC-CAL sequential version, is achieved.

Paper Details

Date Published: 24 October 2016
PDF: 9 pages
Proc. SPIE 10007, High-Performance Computing in Geoscience and Remote Sensing VI, 1000709 (24 October 2016); doi: 10.1117/12.2241648
Show Author Affiliations
D. Madroñal , Univ. Politécnica de Madrid (Spain)
H. Fabelo, Univ. de Las Palmas de Gran Canaria (Spain)
R. Lazcano, Univ. Politécnica de Madrid (Spain)
G. M. Callicó, Univ. de Las Palmas de Gran Canaria (Spain)
E. Juárez, Univ. Politécnica de Madrid (Spain)
C. Sanz , Univ. Politécnica de Madrid (Spain)

Published in SPIE Proceedings Vol. 10007:
High-Performance Computing in Geoscience and Remote Sensing VI
Bormin Huang; Sebastián López; Zhensen Wu; Jose M. Nascimento; Jun Li; Valeriy V. Strotov, Editor(s)

© SPIE. Terms of Use
Back to Top