Outlier detection in scatterometer data: neural network approaches

  • Authors:

  • Robert J. Bullen;Dan Cornford;Ian T. Nabney

  • Affiliations:

  • Neural Computing Research Group, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK and Advanced Technology Centre, BAE SYSTEMS, Sowerby Building 20 ...;Neural Computing Research Group, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK;Neural Computing Research Group, School of Engineering and Applied Science, Aston University, Aston Triangle, Birmingham B4 7ET, UK

  • Venue:
  • Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
  • Year:
  • 2003

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Abstract

Satellite-borne scatterometers are used to measure backscattered micro-wave radiation from the ocean surface. This data may be used to infer surface wind vectors where no direct measurements exist. Inherent in this data are outliers owing to aberrations on the water surface and measurement errors within the equipment. We present two techniques for identifying outliers using neural networks; the outliers may then be removed to improve models derived from the data. Firstly the generative topographic mapping (GTM) is used to create a probability density model; data with low probability under the model may be classed as outliers. In the second part of the paper, a sensor model with input-dependent noise is used and outliers are identified based on their probability under this model.GTM was successfully modified to incorporate prior knowledge of the shape of the observation manifold; howewer, GTM could not learn the double skinned nature of the observation manifold. To learn this double skinned manifold necessitated the use of a sensor model which imposes strong constraints on the mapping. The results using GTM with a fixed noise level suggested the noise level may vary as a function of wind speed. This was confirmed by experiments using a sensor model with input-dependent noise, where the variation in noise is most sensitive to the wind speed input. Both models successfully identified gross outliers with the largest differences between models occurring at low wind speeds.