Application of Fuzzy Lattice Neurocomputing (FLN) in Ocean Satellite Images for Pattern Recognition

  • J. A. Piedra-Fernández
  • M. Cantón-Garbín
  • F. Guindos-Rojas
Part of the Studies in Computational Intelligence book series (SCI, volume 67)

Summary. The main objective of this work is to improve the automated interpretation of ocean satellite images using a fuzzy lattice system that recognizes the most important ocean structures in satellite AVHRR (Advanced Very High Resolution Radiometer) images. This chapter presents a hybrid model based on an expert system segmentation method, a method of correlation-based feature selection, and a few classi.ers including Bayesian nets (BN) and fuzzy lattice neural networks. The results obtained by the fuzzy lattice system are clearly better than the results obtained by ANNs (Artificial Neural Nets), knowledge based reasoning systems, and graphic expert system (GES).

Keywords

Feature Selection Bayesian Network Fuzzy Rule Cyclonic Eddy Linguistic Label 
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.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • J. A. Piedra-Fernández
    • 1
  • M. Cantón-Garbín
    • 1
  • F. Guindos-Rojas
    • 1
  1. 1.Dept Languages and ComputationUniversity of AlmeríaAlmeríaSpain

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