Underground Target Classification From Full-Polarimetric GPR Data Using Deep Convolutional Neural Network With Channel Attention Module

Traditional single-polarimetric ground penetrating radar (GPR) has been widely used for the detection and classification of cavities, pipes, cables, and other subsurface objects. However, as these subsurface objects show similar hyperbolic patterns in B-scan images, their data interpretation remains a challenge. In this letter, we propose an underground target classification method based on full-polarimetric GPR data and a multibranch deep convolutional neural network (CNN) with channel attention modules (CAMs). Here, the full-polarimetric GPR data as input are separately sent into each branch of the network for feature extraction. The CAM is used to improve the network's sensitivity and processing of key features. After fusing these full-polarimetric features, underground target classification is achieved with high accuracy. Our experiments demonstrate that more target information can be obtained by applying full-polarimetric GPR data, which is beneficial for improving target classification accuracy.