ACA Multiagent System for Satellite Image Classification

  • Moisés Espínola
  • José A. Piedra
  • Rosa Ayala
  • Luís Iribarne
  • Saturnino Leguizamón
  • Massimo Menenti
Part of the Advances in Intelligent and Soft Computing book series (AINSC, volume 157)

Abstract

In this paper, we present a multiagent system for satellite image classification. With this aim we will describe a new classification algorithm based on cellular automata called ACA (Algorithm based on Cellular Automata). This algorithm can be modeled by agents. Actually, there are different classification algorithms, such as minimum distance and parallelepiped classifiers, but none is fullreliable in terms of quality. One of the main advantages of ACA is to provide a mechanism which offers a hierarchical classification divided into levels of reliability with a final quality optimized through contextual techniques. Finally, we have developed a multiagent system which allows to classify satellite images in the SOLERES framework.

Keywords

Satellite Image Contextual Information Cellular Automaton Multiagent System Noisy Pixel 
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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References

  1. 1.
    Chuvieco, E., Huete, A.: Fundamentals of satellite remote sensing. CRC Press, Boca Raton (2010)Google Scholar
  2. 2.
    Rees, W.G.: Physical principles of remote sensing, 2nd edn. Cambridge University Press (2001)Google Scholar
  3. 3.
    Schowengerdt, R.A.: Techniques for image processing and classification in remote sensing. Academic Press (1985)Google Scholar
  4. 4.
    Ayala, R., Becerra, A., Flores, I.M., Bienvenido, J.F., Diaz, J.R.: Evaluation of greenhouse covered extensions and required resources with satellite images and GIS. Almeria case. In: Second European Conference of the European Federation for Information Technology in Agriculture, Food and the Environment, Bonn, Germany, pp. 27–30 (1999)Google Scholar
  5. 5.
    Ayala, R., Menenti, M., Girolana, D.: Evaluation methodology for classification process of digital images. In: IEEE Int. Geoscience and Remote Sensing Symposium and the 24th Canadian Symposium on Remote Sensing, IGARSS 2002, Toronto, Canada, pp. 3363–3365 (2002)Google Scholar
  6. 6.
    Wolfram, S.: A new kind of science. Wolfram Media, Inc., Champaign (2002)MATHGoogle Scholar
  7. 7.
    Kari, J.: Theory of cellular automata: a survey. Theoretical Computer Science 334, 3–33 (2005)MathSciNetMATHCrossRefGoogle Scholar
  8. 8.
    Leguizamon, S.: Simulation of snow-cover dynamics using the cellular automata approach. In: 8th Symp. on High Mountain R. Sens. Cartography, pp. 87–91 (2005)Google Scholar
  9. 9.
    Balzter, H., Braun, P., Kuhler, W.: Cellular automata models for vegetation dynamics. Ecological Modelling 107, 113–125 (1998)CrossRefGoogle Scholar
  10. 10.
    Lobitz, B., Beck, L., Huq, A., et al.: Climate and infectious disease: use of remote sensing for detection of Vibrio cholerae by indirect measurement. National Academic of Sci. USA 97(4), 1438–1443 (2000)CrossRefGoogle Scholar
  11. 11.
    Karafyllidis, I., Thanailakis, A.: A model for predicting forest fire spreading using cellular automata. Ecological Modelling 99, 87–97 (1997)CrossRefGoogle Scholar
  12. 12.
    Muzy, A., Innocenti, E., Aiello, A., Santucci, J.F., Santonio, P.A., Hill, D.: Modelling and simulation of ecological propagation processes: application to fire spread. Environmental Modelling and Software 20, 827–842 (2005)CrossRefGoogle Scholar
  13. 13.
    Leguizamon, S.: Modeling land features dynamics by using cellular automata techniques. In: ISPR Technical Comision, pp. 497–501 (2006)Google Scholar
  14. 14.
    Messina, J., Walsh, S.: Simulating land use and land cover dynamics in the ecuadorian Amazon through cellular automata approaches and an integrated GIS. In: Open Meeting of the Human Dimensions of Global Environmental Change Research Community in Rio de Janeiro, Brazil, pp. 6–8 (2001)Google Scholar
  15. 15.
    Popovici, A., Popovici, D.: Cellular automata in image processing. In: 15th Int. Symp. Mathematical Theory of Networks and Systems (2002)Google Scholar
  16. 16.
    Mojaradi, B., Lucas, C., Varshosaz, M.: Using learning cellular automata for post classification satellite imagery. International Archives of Photogrammetry Remote Sensing and Spatial Information Sciences 35(4), 991–995 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Moisés Espínola
    • 1
  • José A. Piedra
    • 1
  • Rosa Ayala
    • 1
  • Luís Iribarne
    • 1
  • Saturnino Leguizamón
    • 2
  • Massimo Menenti
    • 3
  1. 1.Applied Computing GroupUniversity of AlmeríaAlmeriaSpain
  2. 2.Regional Faculty of MendozaNational Technnological UniversityVariousArgentina
  3. 3.Aerospace Engineering Optical and Laser Remote SensingTUDelftDelftThe Netherlands

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