ARTIFICIAL NEURAL-NETWORK ROBUSTNESS FOR ON-BOARD SATELLITE IMAGE-PROCESSING - RESULTS OF UPSET SIMULATIONS AND GROUND TESTS

Authors
VELAZCO R CHEYNET P MULLER JD ECOFFET R BUCHNER S
Citation
R. Velazco et al., ARTIFICIAL NEURAL-NETWORK ROBUSTNESS FOR ON-BOARD SATELLITE IMAGE-PROCESSING - RESULTS OF UPSET SIMULATIONS AND GROUND TESTS, IEEE transactions on nuclear science, 44(6), 1997, pp. 2337-2344
Citations number
15
Categorie Soggetti
Nuclear Sciences & Tecnology","Engineering, Eletrical & Electronic
Journal title
IEEE transactions on nuclear science → ACNP
ISSN journal
00189499
Volume
44
Issue
6
Year of publication
1997
Part
1
Pages
2337 - 2344
Database
ISI
SICI code
0018-9499(1997)44:6<2337:ANRFOS>2.0.ZU;2-1
Abstract
Artificial Neural Networks have been shown to possess fault tolerant p roperties. We present the architecture of a neural network designed to process satellite images (SPOT photos). Computer simulations and grou nd tests performed on a digital implementation of this neural network prove its robustness with respect to bit errors.