In this article a new neural network based method for automatic classification of ground penetrating radar (GPR) traces is proposed. The presented approach is based on a new representation of GPR signals by polynomials approximation. The coefficients of the polynomial (the feature vector) are neural network inputs for automatic classification of a special kind of geologic structure-a sinkhole. The analysis and results show that the classifier can effectively distinguish sinkholes from other geologic structures.
@article{bwmeta1.element.bwnjournal-article-amcv25i4p955bwm,
author = {Piotr Szymczyk and Sylwia Tomecka-Sucho\'n and Magdalena Szymczyk},
title = {Neural networks as a tool for georadar data processing},
journal = {International Journal of Applied Mathematics and Computer Science},
volume = {25},
year = {2015},
pages = {955-960},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-amcv25i4p955bwm}
}
Piotr Szymczyk; Sylwia Tomecka-Suchoń; Magdalena Szymczyk. Neural networks as a tool for georadar data processing. International Journal of Applied Mathematics and Computer Science, Tome 25 (2015) pp. 955-960. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-amcv25i4p955bwm/
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