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Low power compressive sensing for hyperspectral imagery

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Detalhes bibliográficos
Resumo:Hyperspectral imaging instruments allow remote Earth exploration by measuring hundreds of spectral bands at very narrow channels of a given spatial area. The resulting hyperspectral data cube typically comprises several gigabytes. Such extremely large volumes of data introduces problems in its transmission to Earth due to limited communication bandwidth. As a result, the applicability of data compression techniques to hyperspectral images have received increasing attention. This paper, presents a study of the power and time consumption of a parallel implementation for a spectral compressive acquisition method on a Jetson TX2 platform. The conducted experiments have been performed to demonstrate the applicability of these methods for onboard processing. The results show that by using this low energy consumption GPU and integer data type is it possible to obtain real-time performance with a very limited power requirement while maintaining the methods accuracy.
Autores principais:Nascimento, Jose
Outros Autores:Véstias, Mário
Assunto:Hyperspectral imagery Compressive sensing Embedded systems
Ano:2019
País:Portugal
Tipo de documento:documento de conferência
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Lisboa
Idioma:inglês
Origem:Repositório Científico do Instituto Politécnico de Lisboa
Descrição
Resumo:Hyperspectral imaging instruments allow remote Earth exploration by measuring hundreds of spectral bands at very narrow channels of a given spatial area. The resulting hyperspectral data cube typically comprises several gigabytes. Such extremely large volumes of data introduces problems in its transmission to Earth due to limited communication bandwidth. As a result, the applicability of data compression techniques to hyperspectral images have received increasing attention. This paper, presents a study of the power and time consumption of a parallel implementation for a spectral compressive acquisition method on a Jetson TX2 platform. The conducted experiments have been performed to demonstrate the applicability of these methods for onboard processing. The results show that by using this low energy consumption GPU and integer data type is it possible to obtain real-time performance with a very limited power requirement while maintaining the methods accuracy.