A scatterometer neural network sensor model with input noise

Dan Cornford, Guillaume Ramage, Ian T. Nabney

Research output: Contribution to journalArticlepeer-review


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
Pages (from-to)13-21
Number of pages9
Issue number1
Publication statusPublished - Jan 2000


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


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