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Hyperspectral compressive sensing: a comparison of embedded GPU and ARM implementations
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Paper Abstract

Hyperspectral imaging involves the sensing of a large amount of spatial information across several adjacent wavelengths. Typically, hyperspectral images can be represented by a three-dimensional data cube. The collected data cube is extremely large to be transmitted from the satellite/airborne platform to the ground station. Compressive sensing (CS) is an emerging technique that acquire directly the compressed signal instead of acquiring the full data set. This reduces the amount of data that needs to be measured, transmitted and stored in first place. In this paper, a comparison of a CS method implementation for an ARM and for a GPU is conducted. This study takes into account the accuracy, the performance, and the power consumption for both implementations. The 256-cores GPU of a Jetson TX2 board, the dual-core ARM Cortex-A9 of a ZYNQ-7000 SoC FPGA and the quad-core ARM Cortex-A53 of a ZYNQ UltraScale SoC FPGA are the target platforms used for experimental validation. The obtained results indicate that the embedded GPU is faster but uses more power. Therefore, the most appropriate platform depends on the performance and power constraints of the project.

Paper Details

Date Published: 16 October 2019
PDF: 10 pages
Proc. SPIE 11163, Emerging Imaging and Sensing Technologies for Security and Defence IV, 111630H (16 October 2019); doi: 10.1117/12.2532581
Show Author Affiliations
José M. P. Nascimento, Instituto de Telecomunicações (Portugal)
Instituto Superior de Engenharia de Lisboa (Portugal)
Mário Véstias, Instituto Superior de Engenharia de Lisboa (Portugal)
INESC-ID (Portugal)


Published in SPIE Proceedings Vol. 11163:
Emerging Imaging and Sensing Technologies for Security and Defence IV
Gerald S. Buller; Richard C. Hollins; Robert A. Lamb; Martin Laurenzis, Editor(s)

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