A scatterometer neural network sensor model with input noise
Data(s) |
22/10/1998
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Resumo |
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. |
Formato |
application/pdf |
Identificador |
http://eprints.aston.ac.uk/1411/1/NCRG_98_021.pdf Cornford, Dan; Ramage, Guillaume and Nabney, Ian T. (1998). A scatterometer neural network sensor model with input noise. Technical Report. Aston University, Birmingham. |
Publicador |
Aston University |
Relação |
http://eprints.aston.ac.uk/1411/ |
Tipo |
Monograph NonPeerReviewed |