A scatterometer neural network sensor model with input noise

Dan Cornford, Guillaume Ramage, Ian T. Nabney

Research output: Contribution to journalArticle (Academic Journal)peer-review

5 Citations (Scopus)
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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.
Original languageUndefined/Unknown
Pages (from-to)13-21
Number of pages9
JournalNeurocomputing
Volume30
Issue number1
DOIs
Publication statusPublished - 1 Jan 2000

Bibliographical note

See http://eprints.aston.ac.uk/1411/

Keywords

  • non-linear regression, input uncertainty, wind retrieval, scatterometer

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