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Ibero-American Conference on Artificial Intelligence

IBERAMIA 2002: Advances in Artificial Intelligence — IBERAMIA 2002 pp 42–49Cite as

A Comparison of PCA and GA Selected Features for Cloud Field Classification

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Part of the Lecture Notes in Computer Science book series (LNAI,volume 2527)

Abstract

In this work a back propagation neural network (BPNN) is used for the segmentation of Meteosat images covering the Iberian Peninsula. The images are segmented in the classes land (L), sea (S), fog (F), low clouds (CL), middle clouds (CM), high clouds (CH) and clouds with vertical growth (CV). The classification is performed from an initial set of several statistical textural features based on the gray level co-occurrence matrix (GLCM) proposed by Welch [1]. This initial set of features is made up of 144 parameters and to reduce its dimensionality two methods for feature selection have been studied and compared. The first one includes genetic algorithms (GA) and the second is based on principal component analysis (PCA). These methods are conceptually very different. While GA interacts with the neural network in the selection process, PCA only depends on the values of the initial set of features.

Keywords

  • Feature Selection
  • Hide Layer
  • Feature Selection Method
  • Back Propagation Neural Network
  • Probabilistic Neural Network

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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  • DOI: 10.1007/3-540-36131-6_5
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Author information

Authors and Affiliations

  1. Departamento de Electrónica e Ingeniería Electromecánica, Universidad de Extremadura, Avda. de Elvas s/n, 06071, Badajoz, Spain

    Miguel Macías-Macías & Carlos J. García-Orellana

  2. Departamento de Física, Universidad de Extremadura, Avda. de Elvas s/n, 06071, Badajoz, Spain

    Horacio M. González-Velasco, Ramón Gallardo-Caballero & Antonio Serrano-Pérez

Authors
  1. Miguel Macías-Macías
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  2. Carlos J. García-Orellana
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  3. Horacio M. González-Velasco
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  4. Ramón Gallardo-Caballero
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  5. Antonio Serrano-Pérez
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Editor information

Editors and Affiliations

  1. Telefónica Investigación y Desarrollo, Emilio Vargas 6, 28043, Madrid, Spain

    Francisco J. Garijo

  2. Dpto. Lenguajes y Sistemas Informáticos, Universidad de Sevilla, ETS Ingeniería Informática, 41012, Seville, Spain

    José C. Riquelme & Miguel Toro & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Macías-Macías, M., García-Orellana, C.J., González-Velasco, H.M., Gallardo-Caballero, R., Serrano-Pérez, A. (2002). A Comparison of PCA and GA Selected Features for Cloud Field Classification. In: Garijo, F.J., Riquelme, J.C., Toro, M. (eds) Advances in Artificial Intelligence — IBERAMIA 2002. IBERAMIA 2002. Lecture Notes in Computer Science(), vol 2527. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36131-6_5

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  • DOI: https://doi.org/10.1007/3-540-36131-6_5

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  • Online ISBN: 978-3-540-36131-2

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