@article{8a7305846c1b4f2b90f4d45c3e74a5c5,
title = "A scatterometer neural network sensor model with input noise",
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.",
keywords = "non-linear regression, input uncertainty, wind retrieval, scatterometer",
author = "Dan Cornford and Guillaume Ramage and Nabney, {Ian T.}",
year = "2000",
month = jan,
doi = "10.1016/S0925-2312(99)00137-X",
language = "English",
volume = "30",
pages = "13--21",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier",
number = "1",
}