  The reconstruction of the 3D permittivity map from ground-penetrating radar
(GPR) data is of great importance for mapping subsurface environments and
inspecting underground structural integrity. Traditional iterative 3D
reconstruction algorithms suffer from strong non-linearity, ill-posedness, and
high computational cost. To tackle these issues, a 3D deep learning scheme,
called 3DInvNet, is proposed to reconstruct 3D permittivity maps from GPR
C-scans. The proposed scheme leverages a prior 3D convolutional neural network
with a feature attention mechanism to suppress the noise in the C-scans due to
subsurface heterogeneous soil environments. Then a 3D U-shaped encoder-decoder
network with multi-scale feature aggregation modules is designed to establish
the optimal inverse mapping from the denoised C-scans to 3D permittivity maps.
Furthermore, a three-step separate learning strategy is employed to pre-train
and fine-tune the networks. The proposed scheme is applied to numerical
simulation as well as real measurement data. The quantitative and qualitative
results show the network capability, generalizability, and robustness in
denoising GPR C-scans and reconstructing 3D permittivity maps of subsurface
objects.
