Publicación:
Deep mixed precision for hyperspectral image classification

Fecha
2021-02-03
Editor/a
Director/a
Tutor/a
Coordinador/a
Prologuista
Revisor/a
Ilustrador/a
Derechos de acceso
info:eu-repo/semantics/openAccess
Título de la revista
ISSN de la revista
Título del volumen
Editor
Springer
Proyectos de investigación
Unidades organizativas
Número de la revista
Resumen
Hyperspectral images (HSIs) record scenes at different wavelength channels, providing detailed spatial and spectral information. How to storage and process this highdimensional data plays a vital role in many practical applications, where classification technologies have emerged as excellent processing tools. However, their high computational complexity and energy requirements bring some challenges. Adopting low-power consumption architectures and deep learning (DL) approaches has to provide acceptable computing capabilities without reducing accuracy demand. However, most DL architectures employ single-precision (FP32) to train models, and some big DL architectures will have a limitation on memory and computation resources. This can negatively affect the network learning process. This letter leads these challenges by using mixed precision into DL architectures for HSI classification to speed up the training process and reduce the memory consumption/access. Proposed models are evaluated on four widely used data sets. Also, low and highpower consumption devices are compared, considering NVIDIA Jetson Xavier and Titan RTX GPUs, to evaluate the proposal viability in on-board processing devices. Obtained results demonstrate the efficiency and effectiveness of these models within HSI classification task for both devices. Source codes: https ://githu b.com/mhaut / CNN-MP-HSI.
Descripción
The registered version of this article, first published in “Journal of Supercomputing 77, 2021", is available online at the publisher's website: Springer, https://doi.org/10.1007/s11227-021-03638-2 La versión registrada de este artículo, publicado por primera vez en “Journal of Supercomputing 77, 2021", está disponible en línea en el sitio web del editor: Springer, https://doi.org/10.1007/s11227-021-03638-2
Categorías UNESCO
Palabras clave
hyperspectral image, deeplearning, mixed precision
Citación
Paoletti, M.E., Tao, X., Haut, J.M. et al. Deep mixed precision for hyperspectral image classification. J Supercomput 77, 9190–9201 (2021). https://doi.org/10.1007/s11227-021-03638-2
Centro
Facultades y escuelas::E.T.S. de Ingeniería Informática
Departamento
Lenguajes y Sistemas Informáticos
Grupo de investigación
Grupo de innovación
Programa de doctorado
Cátedra