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Authors: Manolo Cruz ; Moisés Espínola ; Rosa Ayala ; Mercedes Peralta and José Antonio Torres

Affiliation: University of Almería, Spain

Keyword(s): Neural network, RBF, Remote sensing, Ecological regionalization.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Artificial Intelligence and Decision Support Systems ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Enterprise Information Systems ; Health Engineering and Technology Applications ; Higher Level Artificial Neural Network Based Intelligent Systems ; Human-Computer Interaction ; Learning Paradigms and Algorithms ; Methodologies and Methods ; Neural Network Software and Applications ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Theory and Methods

Abstract: The aim of this work is to present an application of the Radial Basis Functions Nets (RBFs) for simplifying and reducing the cost of ecological regionalization. The process speeds up and replaces the classic means of obtaining ecological variables through field studies. The radial basis function networks were applied to estimate field data remotely, using data captured by the Landsat satellite and correlating it with ecological variables in order to substitute for them in the regionalization process. This approach substantially reduces the time and cost of ecological regionalization, limiting field studies and automating the generation of the ecological variables. The technique could be applied without restriction to map vegetation in any other area for which satellite coverage exists.

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Paper citation in several formats:
Cruz, M.; Espínola, M.; Ayala, R.; Peralta, M. and Torres, J. (2010). HOW CAN NEURAL NETWORKS SPEED UP ECOLOGICAL REGIONALIZATION FRIENDLY? - Replacement of Field Studies by Satellite Data using RBFs. In Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (IJCCI 2010) - ICNC; ISBN 978-989-8425-32-4, SciTePress, pages 295-300. DOI: 10.5220/0003062402950300

@conference{icnc10,
author={Manolo Cruz. and Moisés Espínola. and Rosa Ayala. and Mercedes Peralta. and José Antonio Torres.},
title={HOW CAN NEURAL NETWORKS SPEED UP ECOLOGICAL REGIONALIZATION FRIENDLY? - Replacement of Field Studies by Satellite Data using RBFs},
booktitle={Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (IJCCI 2010) - ICNC},
year={2010},
pages={295-300},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003062402950300},
isbn={978-989-8425-32-4},
}

TY - CONF

JO - Proceedings of the International Conference on Fuzzy Computation and 2nd International Conference on Neural Computation (IJCCI 2010) - ICNC
TI - HOW CAN NEURAL NETWORKS SPEED UP ECOLOGICAL REGIONALIZATION FRIENDLY? - Replacement of Field Studies by Satellite Data using RBFs
SN - 978-989-8425-32-4
AU - Cruz, M.
AU - Espínola, M.
AU - Ayala, R.
AU - Peralta, M.
AU - Torres, J.
PY - 2010
SP - 295
EP - 300
DO - 10.5220/0003062402950300
PB - SciTePress

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