Advertisement

A Comparative Study of Two Neural Models for Cloud Screening of Iberian Peninsula Meteosat Images

  • Miguel Macías Macías
  • F. Javier López Aligué
  • Antonio Serrano Pérez
  • Antonio Astilleros Vivas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2085)

Abstract

In this work we make a comparative study of the results obtained in the automatic interpretation of the Iberian Peninsula Meteosat images by means of neural networks techniques, in particular, multi-layer perceptrons and self organizing maps. The interpretation of these images implies their segmentation in the classes SEA (S), LAND (L), LOW CLOUDS (CL), MIDDLE CLOUDS (CM), HIGH CLOUDS (CH) and CLOUDS WITH VERTICAL GROWTH (CV).

Keywords

Advanced Very High Resolution Radiometer Advanced Very High Resolution Radiometer Neural Model Geostationary Satellite Learn Vector Quantization 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Debois, M., Seze, G., and Szejwach, G.: Automatic Classification of Clouds on Meteosat Imagery: Application to High-Level Clouds. Journal of Applied Meteorology, 21 (1982) 401–412.CrossRefGoogle Scholar
  2. 2.
    Key, J., Barry, R.G.: Cloud Cover Analysis with Arctic AVHRR Data, 1. Cloud Detection. Journal of Geophysical Research, 94(D15), December 20 (1989) 18521–18535.CrossRefGoogle Scholar
  3. 3.
    Key J.: Cloud Cover Analysis with Arctic Advanced Very High Resolution Radiometer Data, 2. Cassification with Spectral and Textural Measures. Journal of Geophysical Research, 95(D6) May 20 (1990) 7661–7675.CrossRefGoogle Scholar
  4. 4.
    Karlsson, K.G., Liljas E.: The SMHI Model for Cloud and Precipitation Analysis from Multispectral AVHRR Data. Technical Report 10, Swedish Meteorological and Hydrological Institute, August (1990).Google Scholar
  5. 5.
    Welch, R.M., Sengupta, S.K., Goroch, A.K., Rapindra, P., Rangaraj N. and Navar, M.S.: Polar Cloud and Surface Classification Using AVHRR Imagery: An Intercomparison of Methods. Journal of Applied Meteorology, 31, 1992, 405–419.CrossRefGoogle Scholar
  6. 6.
    Livarinen, J., Valkealahti, K., Visa, A., Simula, O.: Feature Selection with Self-Organizing Feature Map. In International Conference on Artificial Neural Networks, Sorrento, Italy, May (1994) 26–29.Google Scholar
  7. 7.
    Visa, A., Valkealahti, K., Livarinen, J., Simula, O.: Experiences from Operational Cloud Classifier Based on Self-Organising Map. In SPIE Vol. 2243 Applications of Artificial Neural Networks V, volume 2243, Orlando, Florida, April 5-8 (1994) 484–495.Google Scholar
  8. 8.
    Bankert, R.L.: Cloud Classification of AVHRR Imagery in Maritime Regions Using a Probabilistic Neural Network. Journal of Applied. Meteorology, 33, (1994) 909–918.CrossRefGoogle Scholar
  9. 9.
    Yhann, Stephan R., Simpson, James J., Application of Neural Networks to AVHRR Cloud Segmentation. IEEE transactions on geoscience and remote sensing, vol 33,No. 3, May (1995).Google Scholar
  10. 10.
    Lee J., Weger R.C., Sengupta S.K. And Welch R.M.: A Neural Network Approach to Cloud Classification. IEEE Transactions on Geoscience and Remote Sensing, 28(5), (1990) 846–855.CrossRefGoogle Scholar
  11. 11.
    Welch, R.M., Sengupta, S.K., and Chen, D.W.: Cloud Field Classification Based Upon High Spatial Resolution Textural Features. Journal of Geophysical Research, Vol. 93, D10, (1988), 12663–12681.CrossRefGoogle Scholar
  12. 12.
    Aha, D.W., and Bankert, R.L.: A Comparative Evaluation of Sequential Feature Selection Algorithms. Artificial Intelligence and Statistics V., D. Fisher and J.H. Lenz, editors. Springer-Verlag, New York, 1996.Google Scholar
  13. 13.
    Stone, M.: Cross-Validatory Choice and Assessment of Statistical predictions. Journal of the Royal Statistical Society, B 36(1), (1974),111–147.Google Scholar
  14. 14.
    Kohonen, T.: The Self-Organizing Map. In Proceedings of the IEEE, Vol. 78,No.9, September (1990).Google Scholar
  15. 15.
    Kohonen, T.: Learning Vector Quantization for Pattern Recognition. Helsinki University of Technology, Department of Technical Physics, Laboratory of Computer and Information Science, Report TKK-F A601, (1986).Google Scholar
  16. 16.
    M. Riedmiller, M., Braun, L.: A Direct Adaptive Method for Faster Backpropagation Learning: The RPROP Algorithm. In Proceedings of the IEEE International Conference on Neural Networks 1993 (ICNN 93), 1993.Google Scholar
  17. 17.
    M.P. Perrone, and L.N., Cooper.: When networks disagree: ensemble methods for hybrid neural networks. In R.J. Mammone (Ed.), Artificial Neural Networks for Speech and Vision, pp. 126–142. London: Cahpman & Hall., 1993Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Miguel Macías Macías
    • 1
  • F. Javier López Aligué
    • 1
  • Antonio Serrano Pérez
    • 2
  • Antonio Astilleros Vivas
    • 2
  1. 1.Departamento de Electrónica e Ingeniería ElectromecánicaUniversidad de ExtremaduraBadajozSpain
  2. 2.Departamento de FísicaUniversidad de ExtremaduraBadajozSpain

Personalised recommendations