KEYWORDS: General packet radio service, Radar signal processing, Data modeling, Neural networks, Convolution, Feature extraction, Education and training, Deep convolutional neural networks, Interpolation, Ground penetrating radar
Ground Penetrating Radar (GPR) is usually used to detect unknown underground structure information, but it is very difficult to extract the underground target structure information from original GPR signal. This paper aims to solve the inversion problem by deep learning method. The two-dimensional ground penetrating radar B-scan signal is converted into intuitive underground structure information by neural network. In this paper, the DeepLab network proposed by Google is improved to solve the problem of permittivity inversion of GPR signal images. We verify the network using simulated data, which is generated by Finite Difference Time Domain (FDTD) algorithm. Finally, we quantitatively evaluate the performance of our network by comparing it with some existing deep learning inversion networks.
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