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

References

• 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.
   Google Scholar
• Becker, J.T., 2014. Deep learning methods for multiband explosive hazard detection using L-Band and X-Band forward-looking ground-penetrating Radar.
   Google Scholar
• Besaw, L.E. and Stimac, P.J., 2015. Deep convolutional neural networks for classifying GPR B-scans. In: Proc. of SPIE. 1–10.
   Google Scholar
• Busby, J.P., et al., 2004. Application of ground penetrating radar to geological investigations. Nottingham: British Geological Survey.
   Google Scholar
• Conyers, L.B, 2015. Analysis and interpretation of GPR datasets for integrated archaeological mapping. Near Surface Geophysics, 13 (6), 645–651. doi: 10.3997/1873-0604.2015018
   Web of Science ®Google Scholar
• Dinh, K., et al., 2015. Improved GPR-based condition assessment of reinforced concrete bridge decks using artificial neural network. CrS Non-Destructive Testing Journal, 5 (2), 3–13.
   Google Scholar
• Dou, Q., et al., 2017. Real-time hyperbola recognition and fitting in GPR data. IEEE Transactions on Geoscience and Remote Sensing, 55 (1), 51–62. doi: 10.1109/TGRS.2016.2592679
   Web of Science ®Google Scholar
• Huang, Z., Pan, Z., and Lei, B, 2017. Transfer learning with deep convolutional neural network for SAR target classification with limited labeled data. Remote Sensing, 9 (9), 1–21.
   Web of Science ®Google Scholar
• Jing, H. and Vladimirova, T., 2017. Novel algorithm for landmine detection using C-scan ground penetrating radar signals. In: international conference on emerging security technologies (EST). 68–73.
   Google Scholar
• Kim, N., et al., 2018. Deep learning-based underground object detection for urban road pavement. International Journal of Pavement Engineering. https://doi.org/10.1080/10298436.2018.1559317.
   Web of Science ®Google Scholar
• Klesk, P., et al., 2015. Fast analysis of C-Scans from ground penetrating radar via 3-D Haar-like features with application to landmine detection. IEEE Transactions on Geoscience and Remote Sensing, 53 (7), 3996–4009. doi: 10.1109/TGRS.2015.2388713
   Web of Science ®Google Scholar
• Krizhevsky, A., Sutskever, I., and Geoffrey, E. H., 2012. Imagenet classification with deep convolutional neural networks. Advances in Neural Information Processing Systems, 25, 1097–1105.
   Google Scholar
• Lameri, S., et al., 2017. Landmine detection from GPR data using convolutional neural networks. In: 25th European Signal Processing Conference. 538–542.
   Google Scholar
• Maas, C. and Schmalzl, J., 2013. Using pattern recognition to automatically localize reflection hyperbolas in data from ground penetrating radar. Computers and Geosciences, 58, 116–125. doi: 10.1016/j.cageo.2013.04.012
   Web of Science ®Google Scholar
• Mayne, P.W., Christopher, B.R., and DeJong, J., 2002. Subsurface investigations — geotechnical site characterization reference manual. Washington, DC: U.S. Department of Transportation Federal Highway Administration.
   Google Scholar
• Núñez-Nieto, X., et al., 2014. GPR signal characterization for automated landmine and UXO detection based on machine learning techniques. Remote Sensing, 6 (10), 9729–9748. doi: 10.3390/rs6109729
   Web of Science ®Google Scholar
• Pan, S. and Yang, Q, 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22 (10), 1345–1359. doi: 10.1109/TKDE.2009.191
   Web of Science ®Google Scholar
• Pasolli, E., Melgani, F., and Donelli, M, 2009. Automatic analysis of GPR images: a pattern-recognition approach. IEEE Transactions on Geoscience and Remote Sensing, 47 (7), 2206–2217. doi: 10.1109/TGRS.2009.2012701
   Web of Science ®Google Scholar
• Qiao, L., et al., 2015. Identification of buried objects in GPR using amplitude modulated signals extracted from multiresolution monogenic signal analysis. Sensors, 15 (12), 30340–30350. doi: 10.3390/s151229801
   PubMed Web of Science ®Google Scholar
• Thitimakorn, T., et al., 2016. Subsurface void detection under the road surface using ground penetrating radar (GPR), a case study in the Bangkok metropolitan area. Thailand. International Journal of Geo-Engineering, 7 (2), 1–9.
   Google Scholar
• Wai-Lok Lai, W., Dérobert, X., and Annan, P., 2017. A review of ground penetrating radar application in civil engineering: A 30-year journey from locating and testing to imaging and diagnosis. NDT and E International, (January), 1–22.
   Google Scholar
• Warren, C., Giannopoulos, A., and Giannakis, I, 2016. Gprmax: open source software to simulate electromagnetic wave propagation for ground penetrating radar. Computer Physics Communications, 209, 163–170. doi: 10.1016/j.cpc.2016.08.020
   Web of Science ®Google Scholar
• Zhang, Y., Huston, D., and Xia, T., 2016. Underground object characterization based on neural networks for ground penetrating radar data. In: Proceedings of the SPIE. 1–9.
   Google Scholar
• Zhao, W., et al., 2018. GPR imaging and characterization of ancient Roman ruins in the Aquileia Archaeological Park, NE Italy. Measurement: Journal of the International Measurement Confederation, 113 (June 2017), 161–171. doi: 10.1016/j.measurement.2017.09.004
   Web of Science ®Google Scholar