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Remote Sens. 2017, 9(10), 973; https://doi.org/10.3390/rs9100973

Parallel Implementation of the CCSDS 1.2.3 Standard for Hyperspectral Lossless Compression

Complutense University of Madrid, Department of Computer Architecture and Automatics, Computer Science Faculty, Complutense University of Madrid, 28040 Madrid, Spain
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Received: 2 August 2017 / Revised: 7 September 2017 / Accepted: 18 September 2017 / Published: 21 September 2017
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Abstract

Hyperspectral imaging is a technology which, by sensing hundreds of wavelengths per pixel, enables fine studies of the captured objects. This produces great amounts of data that require equally big storage, and compression with algorithms such as the Consultative Committee for Space Data Systems (CCSDS) 1.2.3 standard is a must. However, the speed of this lossless compression algorithm is not enough in some real-time scenarios if we use a single-core processor. This is where architectures such as Field Programmable Gate Arrays (FPGAs) and Graphics Processing Units (GPUs) can shine best. In this paper, we present both FPGA and OpenCL implementations of the CCSDS 1.2.3 algorithm. The proposed paralellization method has been implemented on the Virtex-7 XC7VX690T, Virtex-5 XQR5VFX130 and Virtex-4 XC2VFX60 FPGAs, and on the GT440 and GT610 GPUs, and tested using hyperspectral data from NASA’s Airborne Visible Infra-Red Imaging Spectrometer (AVIRIS). Both approaches fulfill our real-time requirements. This paper attempts to shed some light on the comparison between both approaches, including other works from existing literature, explaining the trade-offs of each one. View Full-Text
Keywords: hyperspectral image compression; CCSDS 1.2.3 standard; parallel computing; reconfigurable hardware; field-programmable gate arrays (FPGAs); GPUs; comparison hyperspectral image compression; CCSDS 1.2.3 standard; parallel computing; reconfigurable hardware; field-programmable gate arrays (FPGAs); GPUs; comparison
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).
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Báscones, D.; González, C.; Mozos, D. Parallel Implementation of the CCSDS 1.2.3 Standard for Hyperspectral Lossless Compression. Remote Sens. 2017, 9, 973.

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