Low-High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification
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Título
Low-High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image ClassificationAutoría
Fecha de publicación
2018-11Editor
IEEECita bibliográfica
HAUT, Juan M., et al. Low-High-Power Consumption Architectures for Deep-Learning Models Applied to Hyperspectral Image Classification. IEEE Geoscience and Remote Sensing Letters, 2018.Tipo de documento
info:eu-repo/semantics/articleVersión de la editorial
https://ieeexplore.ieee.org/abstract/document/8554064Versión
info:eu-repo/semantics/acceptedVersionPalabras clave / Materias
Resumen
Convolutional neural networks have emerged as an excellent tool for remotely sensed hyperspectral image (HSI) classification. Nonetheless, the high computational complexity and energy requirements of these models ... [+]
Convolutional neural networks have emerged as an excellent tool for remotely sensed hyperspectral image (HSI) classification. Nonetheless, the high computational complexity and energy requirements of these models typically limit their application in on-board remote sensing scenarios. In this context, low-power consumption architectures are promising platforms that may provide acceptable on-board computing capabilities to achieve satisfactory classification results with reduced energy demand. For instance, the new NVIDIA Jetson Tegra TX2 device is an efficient solution for on-board processing applications using deep-learning (DL) approaches. So far, very few efforts have been devoted to exploiting this or other similar computing platforms in on-board remote sensing procedures. This letter explores the use of low-power consumption architectures and DL algorithms for HSI classification. The conducted experimental study reveals that the NVIDIA Jetson Tegra TX2 device offers a good choice in terms of performance, cost, and energy consumption for on-board HSI classification tasks. [-]
Proyecto de investigación
Generalitat Valenciana (APOSTD/2017/007) ; Spanish Ministry (FPU14/02012- FPU15/02090, ESP2016-79503-C2-2-P) ; Junta de Extremadura (GR15005) ; Spanish MINECO (Grant TIN 2015-65277-R).Derechos de acceso
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