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24 October 2016 Parallel implementation of a hyperspectral image linear SVM classifier using RVC-CAL
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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.
Conference Presentation
© (2016) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
D. Madroñal , H. Fabelo, R. Lazcano, G. M. Callicó, E. Juárez, and C. Sanz "Parallel implementation of a hyperspectral image linear SVM classifier using RVC-CAL", Proc. SPIE 10007, High-Performance Computing in Geoscience and Remote Sensing VI, 1000709 (24 October 2016);

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