A scatterometer neural network sensor model with input noise

Cornford, Dan; Ramage, Guillaume and Nabney, Ian T. (2000). A scatterometer neural network sensor model with input noise. Neurocomputing, 30 (1), pp. 13-21.

Abstract

The ERS-1 satellite carries a scatterometer which measures the amount of radiation scattered back toward the satellite by the ocean's surface. These measurements can be used to infer wind vectors. The implementation of a neural network based forward model which maps wind vectors to radar backscatter is addressed. Input noise cannot be neglected. To account for this noise, a Bayesian framework is adopted. However, Markov Chain Monte Carlo sampling is too computationally expensive. Instead, gradient information is used with a non-linear optimisation algorithm to find the maximum em a posteriori probability values of the unknown variables. The resulting models are shown to compare well with the current operational model when visualised in the target space.

Publication DOI: https://doi.org/10.1016/S0925-2312(99)00137-X
Divisions: Engineering & Applied Sciences > Computer science
Engineering & Applied Sciences > Computer science research group
Engineering & Applied Sciences > Non-linearity and complexity research group
Additional Information: See http://eprints.aston.ac.uk/1411/
Uncontrolled Keywords: non-linear regression,input uncertainty,wind retrieval,scatterometer
Published Date: 2000-01

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