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Outlier detection in scatterometer data: neural network approaches.

Robert J Bullen1, Dan Cornford, Ian T Nabney

  • 1Neural Computing Research Group, School of Engineering and Applied Science, Aston University, Aston Triangle, B4 7ET, Birmingham, UK.

Neural Networks : the Official Journal of the International Neural Network Society
|April 4, 2003
PubMed
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Neural networks identify outliers in ocean surface wind data from satellite scatterometers. Removing these errors improves wind vector models, especially at lower wind speeds.

Area of Science:

  • Oceanography
  • Remote Sensing
  • Data Science

Background:

  • Satellite scatterometers measure ocean surface microwave radiation.
  • This data infers surface wind vectors in data-sparse regions.
  • Outliers in scatterometer data arise from surface aberrations and equipment errors.

Purpose of the Study:

  • To develop and compare neural network techniques for identifying outliers in scatterometer data.
  • To improve the accuracy of wind vector models by removing identified outliers.

Main Methods:

  • Generative Topographic Mapping (GTM) to create a probability density model.
  • A sensor model with input-dependent noise to identify outliers.
  • Comparison of outlier detection capabilities between GTM and the sensor model.

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Main Results:

  • Both GTM and the sensor model successfully identified gross outliers.
  • GTM could not fully capture the double-skinned nature of the observation manifold.
  • The sensor model with input-dependent noise revealed noise variation sensitive to wind speed.
  • Largest discrepancies between models were observed at low wind speeds.

Conclusions:

  • Neural network approaches effectively identify outliers in satellite scatterometer data.
  • Input-dependent noise modeling is crucial for accurately representing scatterometer data, particularly concerning wind speed.
  • Outlier removal enhances the reliability of derived ocean surface wind vector models.