A scatterometer neural network sensor model with input noise

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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.

Details

Original languageEnglish
Pages (from-to)13-21
Number of pages9
JournalNeurocomputing
Volume30
Issue number1
DOIs
Publication statusPublished - Jan 2000

    Keywords

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

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