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Proceedings Paper

Underground object characterization based on neural networks for ground penetrating radar data
Author(s): Yu Zhang; Dryver Huston; Tian Xia
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Paper Abstract

In this paper, an object characterization method based on neural networks is developed for GPR subsurface imaging. Currently, most existing studies demonstrate detecting and imaging objects of cylindrical shapes. While in this paper, no restriction is imposed on the object shape. Three neural network algorithms are exploited to characterize different types of object signatures, including object shape, object material, object size, object depth and subsurface medium’s dielectric constant. Feature extraction is performed to characterize the instantaneous amplitude and time delay of the reflection signal from the object. The characterization method is evaluated utilizing the data synthesized with the finite-difference timedomain (FDTD) simulator.

Paper Details

Date Published: 8 April 2016
PDF: 9 pages
Proc. SPIE 9804, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016, 980403 (8 April 2016); doi: 10.1117/12.2219345
Show Author Affiliations
Yu Zhang, Univ. of Vermont (United States)
Dryver Huston, Univ. of Vermont (United States)
Tian Xia, Univ. of Vermont (United States)


Published in SPIE Proceedings Vol. 9804:
Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016
Tzuyang Yu; Andrew L. Gyekenyesi; Peter J. Shull; H. Felix Wu, Editor(s)

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