  Ground Penetrating Radar (GPR) is one of the most important non-destructive
evaluation (NDE) devices to detect the subsurface objects (i.e. rebars, utility
pipes) and reveal the underground scene. Due to the background noise and
unknown dielectric property of GPR data, the reconstruction of the subsurface
objects with metric information remains the main challenge in GPR inspection.
In this letter, a novel metric GPR migration method is proposed consisting of
three steps to address the above challenge. Firstly, a noise removal
segmentation model is introduced to clear the GPR raw data; furthermore, a
DielectricNet is proposed to predict the dielectric of each GPR B-scan data and
the depth of a target object; at the last step, we optimize the migration
algorithm by using random motion pattern, thus to obtain the 3D metric
migration result. We use both the field and synthetic data to verify the
proposed method, experimental results show that our proposed method is able to
achieve at least 10% improvement in reconstruction accuracy compared with the
conventional migration methods.
