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Artificial neural network robustness for on-board satellite image processing : results of SEU simulations and ground tests

Authors :
Velazco, R.
Cheynet, P.
Muller, J.D.
Ecoffet, R.
Buchner, S.
Techniques of Informatics and Microelectronics for integrated systems Architecture (TIMA)
Institut polytechnique de Grenoble - Grenoble Institute of Technology (Grenoble INP)-Centre National de la Recherche Scientifique (CNRS)-Université Grenoble Alpes (UGA)
CEA (CEA/DAM)
Centre National d'Études Spatiales [Toulouse] (CNES)
SFA Inc/NRL (SFA INC/NRL)
SFA Inc/NRL
Source :
IEEE Nuclear and Space Radiation Effects Conference (NSREC'97), IEEE Nuclear and Space Radiation Effects Conference (NSREC'97), Jul 1997, Snowmass États-Unis
Publication Year :
1997
Publisher :
HAL CCSD, 1997.

Abstract

International audience; Artificial neural networks have been shown to possess fault tolerant properties. We present the architecture of a neural network designed to process satellite images (SPOT photos). Soft simulations and ground tests performed on a digital implementation of this neural network prove its robustness with respect to bit errors.

Details

Language :
French
Database :
OpenAIRE
Journal :
IEEE Nuclear and Space Radiation Effects Conference (NSREC'97), IEEE Nuclear and Space Radiation Effects Conference (NSREC'97), Jul 1997, Snowmass États-Unis
Accession number :
edsair.od......2100..23a5ec5820ade196affaf3e73a1c0cec