Scalable Hardware-Based On-Board Processing for Run-Time Adaptive Lossless Hyperspectral Compression

Rodríguez Medina, Alfonso and Santos, Lucana and Sarmiento, Roberto and Torre Arnanz, Eduardo de la (2019). Scalable Hardware-Based On-Board Processing for Run-Time Adaptive Lossless Hyperspectral Compression. "IEEE Access", v. 7 ; pp. 10644-10652. ISSN 2169-3536. https://doi.org/10.1109/ACCESS.2019.2892308.

Description

Title: Scalable Hardware-Based On-Board Processing for Run-Time Adaptive Lossless Hyperspectral Compression
Author/s:
  • Rodríguez Medina, Alfonso
  • Santos, Lucana
  • Sarmiento, Roberto
  • Torre Arnanz, Eduardo de la
Item Type: Article
Título de Revista/Publicación: IEEE Access
Date: 2019
ISSN: 2169-3536
Volume: 7
Subjects:
Faculty: E.T.S.I. Industriales (UPM)
Department: Automática, Ingeniería Eléctrica y Electrónica e Informática Industrial
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

Hyperspectral data processing is a computationally intensive task that is usually performed in high-performance computing clusters. However, in remote sensing scenarios, where communications are expensive, a compression stage is required at the edge of data acquisition before transmitting information to ground stations for further processing. Moreover, hyperspectral image compressors need to meet minimum performance and energy-efficiency levels to cope with the real-time requirements imposed by the sensors and the available power budget. Hence, they are usually implemented as dedicated hardware accelerators in expensive space-grade electronic devices. In recent years though, these devices have started to coexist with low-cost commercial alternatives in which unconventional techniques, such as run-time hardware reconfiguration are evaluated within research-oriented space missions (e.g., CubeSats). In this paper, a run-time reconfigurable implementation of a low-complexity lossless hyperspectral compressor (i.e., CCSDS 123) on a commercial off-the-shelf device is presented. The proposed approach leverages an FPGA-based on-board processing architecture with a data-parallel execution model to transparently manage a configurable number of resource-efficient hardware cores, dynamically adapting both throughput and energy efficiency. The experimental results show that this solution is competitive when compared with the current state-of-the-art hyperspectral compressors and that the impact of the parallelization scheme on the compression rate is acceptable when considering the improvements in terms of performance and energy consumption. Moreover, scalability tests prove that run-time adaptation of the compression throughput and energy efficiency can be achieved by modifying the number of hardware accelerators, a feature that can be useful in space scenarios, where requirements change over time (e.g., communication bandwidth or power budget).

Funding Projects

TypeCodeAcronymLeaderTitle
Horizon 2020692455ENABLE-S3AVL LIST GMBHEuropean Initiative to Enable Validation for Highly Automated Safe and Secure Systems
Government of SpainPCIN-2015-254UnspecifiedUnspecifiedIniciativa europea para facilitar la validación de sistemas seguros y altamente automatizados

More information

Item ID: 67069
DC Identifier: http://oa.upm.es/67069/
OAI Identifier: oai:oa.upm.es:67069
DOI: 10.1109/ACCESS.2019.2892308
Official URL: https://ieeexplore.ieee.org/document/8610106
Deposited by: Memoria Investigacion
Deposited on: 13 May 2021 15:56
Last Modified: 13 May 2021 15:56
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