The current work studies the relevance of features in time- and frequency-domain given a
scattered Ground Penetrating Radar (GPR) wave. This wave is used to identify inclusions,
such as reinforcement bars and fissures, in concrete structures. The right choice of features
is fundamental to the design of intelligent machines to support the detection, qualification,
and quantification of fissures in concrete. Although the extension of its results to other types
of materials is expected to be possible, this work focuses on the analysis of the problem of
discovering the characteristics of cylindrical materials inside a concrete structure, which
may be conductor, water, or air. Both noiseless and features selected given a white
Gaussian noise are considered in simulated data. Some features were extracted, and those
selected are presented, indicating that the features in time- and frequency-domain are
complementary and relevant. Classification and regression models with different number
of features indicate that not all features available are needed to achieve satisfactory performance.
Moreover, decreasing the number of features also decreases the computational
burden.
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