  Ground Penetrating Radar (GPR) has been widely used to estimate the healthy
operation of some urban roads and underground facilities. When identifying
subsurface anomalies by GPR in an area, the obtained data could be unbalanced,
and the numbers and types of possible underground anomalies could not be
acknowledged in advance. In this paper, a novel method is proposed to improve
the subsurface anomaly detection from GPR B-scan images. A normal (i.e. without
subsurface objects) GPR image section is firstly collected in the detected
area. Concerning that the GPR image is essentially the representation of
electromagnetic (EM) wave and propagation time, and to preserve both the
subsurface background and objects' details, the normal GPR image is segmented
and then fused with simulated GPR images that contain different kinds of
objects to generate the synthetic data for the detection area based on the
wavelet decompositions. Pre-trained CNNs could then be fine-tuned with the
synthetic data, and utilized to extract features of segmented GPR images
subsequently obtained in the detection area. The extracted features could be
classified by the one-class learning algorithm in the feature space without
pre-set anomaly types or numbers. The conducted experiments demonstrate that
fine-tuning the pre-trained CNN with the proposed synthetic data could
effectively improve the feature extraction of the network for the objects in
the detection area. Besides, the proposed method requires only a section of
normal data that could be easily obtained in the detection area, and could also
meet the timeliness requirements in practical applications.
