LAUSR

Hyperbola detection is an important application field of ground-penetrating radar (GPR) systems as underground threats and targets detected by these systems are presented in the form of a hyperbola. However, the low-amplitude hyperbola detection of deeply-buried targets and targets with low metal content has remained a major challenge due to the increase in the attenuation of radar echo with an increase in the detection depth and the features with low resolution extracted by existing methods. In this study, first, a high-level visual feature, namely, phase symmetry, is proposed to effectively improve the feature resolution and the robustness of amplitude change in GPR images. Then, we propose a handcrafted feature descriptor based on phase symmetry, namely, histogram of oriented vector phase symmetry (HOVPS) to improve the detection of hyperbola in GPR images. In constructing HOVPS, we first cite our previous work to enhance the descriptive ability of hyperbola in GPR images. Subsequently, we extend a phase symmetry model to develop a vector feature model [namely, vector phase symmetry (VPS)]. Finally, HOVPS is developed based on the VPS to extract structural information from GPR images. The proposed HOVPS describes the shape features in GPR images using a symmetrical structure, and it is used as the feature input of the classifier for hyperbola detection. The qualitative analysis of the proposed method is performed by comparing the performance of the proposed method for the extraction of features on different GPR data with those of other methods. In addition, we also provide quantitative analysis on different signal-to-noise ratio (SNR) test sets, and the results reveal that the proposed method outperforms various state-of-the-art methods (e.g., GPR histogram of oriented gradient (gprHOG), edge histogram descriptor (EHD), and log-Gabor (LG) feature).