A novel 3D GPR image arrangement for deep learning-based underground object classification-Reference-Cited by-同舟云学术

A novel 3D GPR image arrangement for deep learning-based underground object classification

Published:2019-08-02 Issue:6 Volume:22 Page:740-751
ISSN:1029-8436
Container-title:International Journal of Pavement Engineering
language:en
Short-container-title:International Journal of Pavement Engineering

Author:

Kim Namgyu¹,Kim Sehoon¹,An Yun-Kyu²,Lee Jong-Jae¹^ORCID

Affiliation:

1. Department of Civil and Environmental Engineering, Sejong University, Seoul, Republic of Korea

2. Department of Architectural Engineering, Sejong University, Seoul, Republic of Korea

Publisher

Informa UK Limited

Subject

Mechanics of Materials,Civil and Structural Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/10298436.2019.1645846

Reference24 articles.

1. Barr, G.L., Geological Survey (U.S.), and Pinellas County (Fla.), 1993. Application of Ground-penetrating Radar Methods in Determining Hydrogeologic Conditions in a Karst Area, West-Central Florida. Water-Resources Investigations Report.

2. Becker, J.T., 2014. Deep learning methods for multiband explosive hazard detection using L-Band and X-Band forward-looking ground-penetrating Radar.

3. Besaw, L.E. and Stimac, P.J., 2015. Deep convolutional neural networks for classifying GPR B-scans. In: Proc. of SPIE. 1–10.

4. Analysis and interpretation of GPR datasets for integrated archaeological mapping

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