Hyperbola Detection with RetinaNet and Comparison of Hyperbola Fitting Methods in GPR Data from an Archaeological Site
نویسندگان
چکیده
Hyperbolic diffractions in Ground Penetrating Radar (GPR) data are caused by a variety of subsurface objects such as pipes, stones, or archaeological artifacts. Supplementary to their location, the propagation velocity electromagnetic waves can be derived. In recent years, it was shown that deep learning tools automatically detect hyperbola radargrams using measured over urban infrastructure, which relatively clear. contrast, this study, we used an dataset with diverse underground structures. first step network RetinaNet and achieved average precision 0.58. next step, 10 different approaches for fitting thus determination were applied. The derived information validated manually determined velocities apex points. It extraction threshold column connection clustering (C3) algorithm followed simple is best method, had mean error 0.021 m/ns compared manual determination. 1D velocity-depth distribution ns intervals shape comparable one, but systematic shift about 0.01 towards higher velocities.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2022
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs14153665