A runtime-scalable and hardware-accelerated approach to on-board linear unmixing of hyperspectral images
Space missions are facing disruptive innovation since the appearance of small, lightweight, and low-cost satellites (e.g., CubeSats). The use of commercial devices and their limitations in cost usually entail a decrease in available on-board computing power. To face this change, the on-board process...
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Online Access: | http://hdl.handle.net/10553/54963 https://doi.org/10.3390/rs10111790 |
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ftunivlaspalmas:oai:accedacris.ulpgc.es:10553/54963 2023-05-15T15:25:32+02:00 A runtime-scalable and hardware-accelerated approach to on-board linear unmixing of hyperspectral images Ortiz, Alberto Guerra Hernández, Raúl Celestino Lopez, Sebastian Otero, Andrés Sarmiento Rodríguez, Roberto de la Torre, Eduardo Rodríguez, Alfonso 57204770113 56972626600 56333613300 57187722000 35868116400 35609452100 6603668216 29777603 31468442 2216671 465777 298843 116294 626981 WOS:Ortiz, A WOS:Rodriguez, Alfonso WOS:Guerra, R WOS:Lopez, S WOS:Otero, A WOS:Sarmiento, R WOS:de la Torre, E BU-TEL 2018 http://hdl.handle.net/10553/54963 https://doi.org/10.3390/rs10111790 eng eng Remote Sensing 10 2072-4292 WoS http://hdl.handle.net/10553/54963 doi:10.3390/rs10111790 85057127653 000451733800121 1790 Sí Remote Sensing [2072-4292],v. 10 (1790) 3307 Tecnología electrónica Parallel Implementation Fast Algorithm Classification Capabilities Compression Satellites Hybrid Chain Hyperspectral Imaging Linear Unmixing Fpgas On-Board Processing Artico(3) info:eu-repo/semantics/article Article 2018 ftunivlaspalmas https://doi.org/10.3390/rs10111790 2022-03-16T00:11:56Z Space missions are facing disruptive innovation since the appearance of small, lightweight, and low-cost satellites (e.g., CubeSats). The use of commercial devices and their limitations in cost usually entail a decrease in available on-board computing power. To face this change, the on-board processing paradigm is advancing towards the clustering of satellites, and moving to distributed and collaborative schemes in order to maintain acceptable performance levels in complex applications such as hyperspectral image processing. In this scenario, hybrid hardware/software and reconfigurable computing have appeared as key enabling technologies, even though they increase complexity in both design and run time. In this paper, the ARTICo(3) framework, which abstracts and eases the design and run-time management of hardware-accelerated systems, has been used to deploy a networked implementation of the Fast UNmixing (FUN) algorithm, which performs linear unmixing of hyperspectral images in a small cluster of reconfigurable computing devices that emulates a distributed on-board processing scenario. Algorithmic modifications have been proposed to enable data-level parallelism and foster scalability in two ways: on the one hand, in the number of accelerators per reconfigurable device; on the other hand, in the number of network nodes. Experimental results motivate the use of ARTICo(3)-enabled systems for on-board processing in applications traditionally addressed by high-performance on-Earth computation. Results also show that the proposed implementation may be better, for certain configurations, than an equivalent software-based solution in both performance and energy efficiency, achieving great scalability that is only limited by communication bandwidth. 21 1,43 4,118 Q1 Q1 SCIE Article in Journal/Newspaper artico Universidad de Las Palmas de Gran Canaria: Acceda Remote Sensing 10 11 1790 |
institution |
Open Polar |
collection |
Universidad de Las Palmas de Gran Canaria: Acceda |
op_collection_id |
ftunivlaspalmas |
language |
English |
topic |
3307 Tecnología electrónica Parallel Implementation Fast Algorithm Classification Capabilities Compression Satellites Hybrid Chain Hyperspectral Imaging Linear Unmixing Fpgas On-Board Processing Artico(3) |
spellingShingle |
3307 Tecnología electrónica Parallel Implementation Fast Algorithm Classification Capabilities Compression Satellites Hybrid Chain Hyperspectral Imaging Linear Unmixing Fpgas On-Board Processing Artico(3) Ortiz, Alberto Guerra Hernández, Raúl Celestino Lopez, Sebastian Otero, Andrés Sarmiento Rodríguez, Roberto de la Torre, Eduardo Rodríguez, Alfonso A runtime-scalable and hardware-accelerated approach to on-board linear unmixing of hyperspectral images |
topic_facet |
3307 Tecnología electrónica Parallel Implementation Fast Algorithm Classification Capabilities Compression Satellites Hybrid Chain Hyperspectral Imaging Linear Unmixing Fpgas On-Board Processing Artico(3) |
description |
Space missions are facing disruptive innovation since the appearance of small, lightweight, and low-cost satellites (e.g., CubeSats). The use of commercial devices and their limitations in cost usually entail a decrease in available on-board computing power. To face this change, the on-board processing paradigm is advancing towards the clustering of satellites, and moving to distributed and collaborative schemes in order to maintain acceptable performance levels in complex applications such as hyperspectral image processing. In this scenario, hybrid hardware/software and reconfigurable computing have appeared as key enabling technologies, even though they increase complexity in both design and run time. In this paper, the ARTICo(3) framework, which abstracts and eases the design and run-time management of hardware-accelerated systems, has been used to deploy a networked implementation of the Fast UNmixing (FUN) algorithm, which performs linear unmixing of hyperspectral images in a small cluster of reconfigurable computing devices that emulates a distributed on-board processing scenario. Algorithmic modifications have been proposed to enable data-level parallelism and foster scalability in two ways: on the one hand, in the number of accelerators per reconfigurable device; on the other hand, in the number of network nodes. Experimental results motivate the use of ARTICo(3)-enabled systems for on-board processing in applications traditionally addressed by high-performance on-Earth computation. Results also show that the proposed implementation may be better, for certain configurations, than an equivalent software-based solution in both performance and energy efficiency, achieving great scalability that is only limited by communication bandwidth. 21 1,43 4,118 Q1 Q1 SCIE |
author2 |
57204770113 56972626600 56333613300 57187722000 35868116400 35609452100 6603668216 29777603 31468442 2216671 465777 298843 116294 626981 WOS:Ortiz, A WOS:Rodriguez, Alfonso WOS:Guerra, R WOS:Lopez, S WOS:Otero, A WOS:Sarmiento, R WOS:de la Torre, E BU-TEL |
format |
Article in Journal/Newspaper |
author |
Ortiz, Alberto Guerra Hernández, Raúl Celestino Lopez, Sebastian Otero, Andrés Sarmiento Rodríguez, Roberto de la Torre, Eduardo Rodríguez, Alfonso |
author_facet |
Ortiz, Alberto Guerra Hernández, Raúl Celestino Lopez, Sebastian Otero, Andrés Sarmiento Rodríguez, Roberto de la Torre, Eduardo Rodríguez, Alfonso |
author_sort |
Ortiz, Alberto |
title |
A runtime-scalable and hardware-accelerated approach to on-board linear unmixing of hyperspectral images |
title_short |
A runtime-scalable and hardware-accelerated approach to on-board linear unmixing of hyperspectral images |
title_full |
A runtime-scalable and hardware-accelerated approach to on-board linear unmixing of hyperspectral images |
title_fullStr |
A runtime-scalable and hardware-accelerated approach to on-board linear unmixing of hyperspectral images |
title_full_unstemmed |
A runtime-scalable and hardware-accelerated approach to on-board linear unmixing of hyperspectral images |
title_sort |
runtime-scalable and hardware-accelerated approach to on-board linear unmixing of hyperspectral images |
publishDate |
2018 |
url |
http://hdl.handle.net/10553/54963 https://doi.org/10.3390/rs10111790 |
genre |
artico |
genre_facet |
artico |
op_source |
Remote Sensing [2072-4292],v. 10 (1790) |
op_relation |
Remote Sensing 10 2072-4292 WoS http://hdl.handle.net/10553/54963 doi:10.3390/rs10111790 85057127653 000451733800121 1790 Sí |
op_doi |
https://doi.org/10.3390/rs10111790 |
container_title |
Remote Sensing |
container_volume |
10 |
container_issue |
11 |
container_start_page |
1790 |
_version_ |
1766356153273417728 |