Underground Anomaly Detection in GPR Data by Learning in the C3 Model Space
Xiren Zhou, Shikang Liu, Ao Chen, Qiuju Chen, Fang Xiong, Yumin Wang, Huanhuan Chen
Abstract
Ground Penetrating Radar (GPR) provides an effective means for underground anomaly detection, but is also accompanied by some practical issues such as the lack of prior knowledge, insufficient labeled data, and timeliness constraints. In this paper, we propose detecting underground anomalies by adequately capturing multi-directional changing information within GPR B-scan data, which extends the framework of Model-Space Learning (MSL). MSL aims to transform the data from the data space to the model space by representing the original data with fitted models that capture the changes within the data. In GPR data, due to the continuity of the underground medium and electromagnetic wave, there is not only effective changing information in each column of data along the vertical direction, but also in the horizontal detecting direction according to the subsurface environments and existing underground structures. To fully capture the changing information within GPR data, we fit the GPR data along multiple directions respectively, and synthesize the fitted models into a Comprehensive-Change-Captured model (C3 model) wherein multi-directional changing information within the original data is captured. Representing the original data with the C3 model transforms the data from the data space to the C3 model space. The distance metric between C3 models is then introduced, and learning methods could be efficiently implemented on the models. Experiments are conducted on GPR data collected along urban roads, with or without prior knowledge about the existing subsurface anomalies. The obtained results demonstrate the effectiveness of the proposed method.