A scatterometer neural network sensor model with input noise

Dan Cornford, Guillaume Ramage, Ian T. Nabney

Research output: Preprint or Working paperTechnical report

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 languageEnglish
Place of PublicationBirmingham
PublisherAston University
Number of pages8
ISBN (Print)NCRG/98/021
Publication statusPublished - 22 Oct 1998

Keywords

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

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  • Research Output

    A scatterometer neural network sensor model with input noise

    Cornford, D., Ramage, G. & Nabney, I. T., Jan 2000, In : Neurocomputing. 30, 1, p. 13-21 9 p.

    Research output: Contribution to journalArticle

  • Cite this

    Cornford, D., Ramage, G., & Nabney, I. T. (1998). A scatterometer neural network sensor model with input noise. Aston University.