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A Novel Feature Descriptor for Hyperbola Recognition in GPR Images Based on Symmetry Model | IEEE Journals & Magazine | IEEE Xplore

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


Abstract:

Ground penetrating radar (GPR) images typically depict underground targets as hyperbolas, which pose a challenging detection task due to their low amplitude and resolutio...Show More

Abstract:

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.
Published in: IEEE Geoscience and Remote Sensing Letters ( Volume: 20)
Article Sequence Number: 3508405
Date of Publication: 05 July 2023

ISSN Information:


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