Abstract
Images from ground penetrating radar (GPR) may be obscured by high clutter noise over the target signal, making target detection difficult. In this contribution, a decluttering technique is applied to GPR images. This clutter removal/reduction is achieved through wavelet decomposition and application of two methods using the third and fourth order statistics: skewness and kurtosis. These higher order statistics remove clutter but retain target signatures. Different scenarios are considered for real GPR images collected in our controlled lab environment set up and peak signal to noise ratio are compared for the two methods. Further features of targets and non-targets are extracted from de-noised images. These features are used in training a neural network classifier. This classifier is applied to various real GPR images with promising results for detection of targets.
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