Feature Extraction of Structures in Sea Water Using Self-Organizing Maps and Electromagnetic Waves

Murilo Teixeira Silva, Lurimar Smera Batista, Frederico Medeiros Vasconcelos de Albuquerque


The use of Self-Organizing Map (SOM) algorithm for feature extraction and dimensionality reduction applied to underwater object detection with Low Frequency Electromagnetic Waves is presented. Computer simulation is used to generate a direct model for the study region, and a Self Organizing Map Algorithm is used to fit the data and return a similar model, with smaller dimensionality and same characteristics. Results show that virtual sensors are created by the SOM algorithm with consistent predictions, filling the resolution gap of the input data. These results are useful for fastening decision making algorithms by reducing the number of inputs to a group of significant data.


Neural networks; Self organizing maps; Electromagnetism; Underwater environment

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DOI: https://doi.org/10.5540/tema.2015.016.03.0261

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Trends in Computational and Applied Mathematics

A publication of the Brazilian Society of Applied and Computational Mathematics (SBMAC)


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