Identificador persistente para citar o vincular este elemento: http://hdl.handle.net/10553/114113
Título: Performance evaluation of state-of-The-Art CNN architectures for the on-board processing of remotely sensed images
Autores/as: Neris Tomé, Romén 
Guerra, Rael
Lopez, Sebastian 
Sarmiento, Roberto 
Clasificación UNESCO: 3307 Tecnología electrónica
Palabras clave: Deep Learning
Machine Learning
Neural Networks
Remote Sensing
Target Detection
Fecha de publicación: 2021
Publicación seriada: Proceedings (Conference on Design of Circuits and Integrated Systems) 
Conferencia: 36th Conference on Design of Circuits and Integrated Systems - DCIS 2021
Resumen: Over the last few years, Convolutional Neural Networks (CNNs) have been extensively used in different remote sensing applications. However, for large networks the computation and memory requirements have brought many challenges into this field. Additionally, the computational capabilities of hardware devices available on-board satellites is limited, being this another constraint for these implementations. In this paper, the authors present the evaluation of nine different CNN architectures for ship and airplane detection, taking into consideration that the final use-case application will be an on-board system with target detection capabilities.
URI: http://hdl.handle.net/10553/114113
ISBN: 9781665421164
DOI: 10.1109/DCIS53048.2021.9666179
Fuente: 36th Conference on Design of Circuits and Integrated Systems, DCIS 2021 [EISSN 2640-5563], (Enero 2021)
Colección:Actas de congresos
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