Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/114632
Título: FPGA-based implementation of a CNN architecture for the on-board processing of very high resolution remote sensing images
Autores/as: Neris Tomé, Romén 
Rodriguez Molina, Adrian 
Guerra, Raul 
Lopez, Sebastian 
Sarmiento, Roberto 
Clasificación UNESCO: 220990 Tratamiento digital. Imágenes
Palabras clave: Computer Architecture
Convolutional Neural Networks
Convolutional Neural Networks
Deep Learning
Earth, et al.
Fecha de publicación: 2022
Proyectos: Ministerio de Ciencia e Innovacin (Grant Number: PID2020-116417RB-C42)
European Commission (Grant Number: 870485)
Publicación seriada: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 
Resumen: Over the last years, Convolutional Neural Networks (CNNs) have been widely used in remote sensing applications, such as marine surveillance, traffic management or road networks detection. However, since CNNs have extremely high computation, bandwith and memory requirements, the hardware implementation of a CNN on space-grade devices like FPGAs for the on-board processing of the acquired images has brought many challenges. In fact, FPGAs show great advantages in terms of reconfigurability and performance-power ratio, whichmakes them a suitable election to deploy CNNs, although the computational capabilities of this type of hardware devices available on-board is limited, so implementations have to be carefully planned. In this paper, the authors present their work towards the implementation of an efficient CNN onto a spacegrade FPGA in order to achieve the on-board processing of very-high resolution remotely sensed images as soon as the data are provided by the sensor. All this work has been conducted within the EU-funded VIDEO project. As it will be presented in this paper, the work includes the introduction of a methodology based on the project constraints, the evaluation of different state-of-the-art CNN architectures by means of a new efficiency measurement also proposed in this work, the introduction of a new efficient CNN architecture, and finally, its optimized hardware implementation by means of high-level synthesis tools.
URI: http://hdl.handle.net/10553/114632
ISSN: 1939-1404
DOI: 10.1109/JSTARS.2022.3169330
Fuente: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing[ISSN 1939-1404], n. 15, p. 3740-3750
Colección:Artículos
miniatura
Adobe PDF (19,49 MB)
Vista completa

Citas SCOPUSTM   

7
actualizado el 24-mar-2024

Visitas

104
actualizado el 23-mar-2024

Descargas

247
actualizado el 23-mar-2024

Google ScholarTM

Verifica

Altmetric


Comparte



Exporta metadatos



Los elementos en ULPGC accedaCRIS están protegidos por derechos de autor con todos los derechos reservados, a menos que se indique lo contrario.