A Novel Feature Descriptor for Hyperbola Recognition in GPR Images Based on Symmetry Model.

Ground penetrating radar (GPR) images typically depict underground targets as hyperbolas, which pose a challenging detection task due to their low amplitude and resolution. To address this, we propose a robust and efficient feature descriptor based on a modified phase symmetry (PS) model. Specifically, we enhance the PS model to better represent hyperbolas in GPR images and introduce a weighted PS histogram descriptor (WPSHD) as a local structure descriptor. The proposed descriptor is used as the feature input to the classifier to realize the hyperbola recognition. The proposed method is compared with two baselines and state-of-the-art (SOTA) methods, such as histogram of oriented gradient (HOG), edge histogram descriptor (EHD), and histogram of oriented vector PS (HOVPS). Our validation experiments on both public datasets and real-world data show that our proposed algorithm improves hyperbola detection in GPR images, as demonstrated by qualitative and quantitative analyses. [ABSTRACT FROM AUTHOR]