Abstract
Nowadays, remote sensing is used in many environmental applications, helping to solve and improve the social problems derived from them. Examples of remotely sensed applications include soil quality studies, water resources searching, environmental protection or meteorology simulations. The classification algorithms are one of the most important techniques used in remote sensing that help developers to interpret the information contained in the satellite images. At present, there are several classification processes, i.e., maximum likelihood, paralelepiped or minimum distance classifier. In this paper we investigate a new satellite image classification Algorithm based on Cellular Automata (ACA), a technique usually used by researchers on complex systems. There are not previous works related to satellite image classification with cellular automata. This new kind of satellite image classifier, that improves the results obtained by classical algorithms in several aspects, has been validated and experimented in the SOLERES framework.
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Ayala, R., Menenti, M., Girolana, D.: Evaluation methodology for classification process of digital images Igarss 2002. In: IEEE Int. Geoscience and Remote Sensing Symposium and the 24th Canadian Symposium on Remote Sensing, Toronto, Canada, pp. 3363–3365 (2002)
Ayala, R., Becerra, A., Flores, I.M., Bienvenido, J.F., Díaz, J.R.: Evaluation of greenhouse covered extensions and required resources with satellite images and GIS. Almería´s case. In: Second European Conference of the European Federation for Information Technology in Agriculture, Food and the Environment, Bonn, Germany, pp. 27–30 (1999)
Balzter, H., Braun, P., Kühler, W.: Cellular automata models for vegetation dynamics. Ecological Modelling 107, 113–125 (1998)
Leguizamón, S.: Modeling land features dynamics by using cellular automata techniques. In: Proceedings of the ISPR Technical Comision 7 Mid-Term Symposium “From pixels to Processes”, Enschede, The Netherlands, pp. 497–501 (2006)
Leguizamón, S.: Simulation of snow-cover dynamics using the cellular automata approach. In: Proceedings of the 8th International Symposium on High Mountain Remote Sensing Cartography, La Paz, Bolivia, pp. 87–91 (2005)
Lobitz, B., Beck, L., Huq, A., Woods, B., Fuchs, G., Faruque, A., Colwell, R.: Climate and infectious disease: use of remote sensing for detection of Vibrio cholerae by indirect measurement. Proceedings of the National Academic of Sciences of the USA 97(4), 1438–1443 (2000)
Maji, P., Shaw, C., Ganguly, N., Sikdar, B., Chaudhuri, P.: Theory and application of cellular automata for pattern classification. Fundamenta Informaticae 58(3-4), 321–354 (2003)
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)
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)
Moreno, N., Quintero, R., Ablan, M., Barros, R., Dávila, J., Ramírez, H., Tonella, G., Acevedo, M.: Biocomplexity of deforestation in the Caparo tropical forest reserve in Venezuela: an integrated multi-agent and cellular automata model. Environmental Modelling and Software 22, 664–673 (2007)
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)
Muzy, A., Innocenti, E., Aiello, A., Santucci, J.F., Santonio, P.A., Hill, D.: Dynamic structure cellular automata in a fire spreading application. In: First International Conference on Informatics in Control, Automation and Robotics, Setubal, Portugal, pp. 143–151 (2004)
Popovici, A., Popovici, D.: Cellular automata in image processing. In: Proceedings of the 15th International Symposium on the Mathematical Theory of Networks and Systems, 6p. (2002)
Rees, W.G.: Physical principles of remote sensing, 2nd edn. Cambridge University Press, Cambridge (2001)
Schowengerdt, R.A.: Techniques for image processing and classification in remote sensing. Academic Press, London (1985)
Wang, J., Kropff, M., Lammert, B., Christensen, S., Hansen, P.: Using CA model to obtain insight into mechanism of plant population spread in a controllable system: annual weeds as an example. Ecological Modelling 166, 277–286 (2003)
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Espínola, M., Ayala, R., Leguizamón, S., Iribarne, L., Menenti, M. (2010). Cellular Automata Applied in Remote Sensing to Implement Contextual Pseudo-fuzzy Classification. In: Bandini, S., Manzoni, S., Umeo, H., Vizzari, G. (eds) Cellular Automata. ACRI 2010. Lecture Notes in Computer Science, vol 6350. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15979-4_33
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DOI: https://doi.org/10.1007/978-3-642-15979-4_33
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