Please use this identifier to cite or link to this item: http://hdl.handle.net/10553/54963
Title: A runtime-scalable and hardware-accelerated approach to on-board linear unmixing of hyperspectral images
Authors: Ortiz, Alberto
Guerra Hernández, Raúl Celestino 
Lopez, Sebastian 
Otero, Andrés
Sarmiento Rodríguez, Roberto 
de la Torre, Eduardo
Rodríguez, Alfonso
UNESCO Clasification: 3307 Tecnología electrónica
Keywords: Parallel Implementation
Fast Algorithm
Classification
Capabilities
Compression, et al
Issue Date: 2018
Project: Plataforma Hw/Sw Distribuida Para El Procesamiento Inteligente de Información Sensorial Heterogenea en Aplicaciones de Supervisión de Grandes Espacios Naturales 
TEC2017-86722-C4-2-R PLATAFORMA HW/SW DISTRIBUIDA PARA EL PROCESAMIENTO INTELIGENTE DE INFORMACION SENSORIAL HETEROGENEA EN APLICACIONES DE SUPERVISION DE GRANDES ESPACIOS NATURALES
Journal: Remote Sensing 
Abstract: 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.
URI: http://hdl.handle.net/10553/54963
ISSN: 2072-4292
DOI: 10.3390/rs10111790
Source: Remote Sensing [ISSN 2072-4292], v. 10(11), 1790
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