### Abstract

Original language | English |
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Place of Publication | Birmingham |

Publisher | Aston University |

Number of pages | 8 |

ISBN (Print) | NCRG/98/021 |

Publication status | Published - 22 Oct 1998 |

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### Keywords

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

### Cite this

*A scatterometer neural network sensor model with input noise*. Birmingham: Aston University.

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**A scatterometer neural network sensor model with input noise.** / Cornford, Dan; Ramage, Guillaume; Nabney, Ian T.

Research output: Working paper › Technical report

TY - UNPB

T1 - A scatterometer neural network sensor model with input noise

AU - Cornford, Dan

AU - Ramage, Guillaume

AU - Nabney, Ian T.

PY - 1998/10/22

Y1 - 1998/10/22

N2 - 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.

AB - 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.

KW - non-linear regression

KW - input uncertainty

KW - wind retrieval

KW - scatterometer

M3 - Technical report

SN - NCRG/98/021

BT - A scatterometer neural network sensor model with input noise

PB - Aston University

CY - Birmingham

ER -