A Multi-Scale Weighted Back Projection Imaging Technique for Ground Penetrating Radar Applications
Abstract
:1. Introduction
2. Multi-Scale Weighted Back Projection Algorithm
2.1. Traditional BP Imaging Algorithm
2.2. Multi-Scale Weighted BP Imaging Algorithm
2.2.1. Multi-Scale Processing
(1) Initial sampling grid
(2) PTR delination
(3) Hierarchical refinement coefficient
(4) Iteration stop condition
2.2.2. Weighted Factor
3. Experiments and Imaging Results
Example 1
Example 2
Example 3
4. Conclusions
Acknowledgments
Conflicts of Interest
- Author ContributionsWentai Lei presented the idea of multi-scale weighted BP imaging technique and carried out simulation experiments. Ronghua Shi carried out on-site GPR experiment and collected real GPR data. Jian Dong provided GPR simulation code and carried out GPR simulation based on FDTD technique. Yujia Shi carried out code implementation of multi-scale weighted BP imaging technique.
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GPR Scanning Parameters | P = 81 | Δx = 2 cm |
1st iteration | a1 = 8 | Δx1 = 16 cm |
2nd iteration | k2 = 0.4; a2 = 4 | Δx2 = 4 cm |
3rd iteration | k3 = 0.5; a3 = 3 | Δx3 = 1.33 cm |
GPR Scanning Parameters | P = 81 | Δx = 2 cm |
1st iteration | a1 = 5.5 | Δx1 = 10.67 cm |
2nd iteration | k2 = 0.5; a2 = 6 | Δx2 = 1.78 cm |
GPR Scanning Parameters | P = 101 | Δx = 3.45 cm |
1st iteration | a1 = 10 | Δx1 = 34.5 cm |
2nd iteration | k2 = 0.6; a2 = 3 | Δx2 = 11.5 cm |
3rd iteration | k3 = 0.6; a3 = 4 | Δx3 = 2.88 cm |
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Lei, W.; Shi, R.; Dong, J.; Shi, Y. A Multi-Scale Weighted Back Projection Imaging Technique for Ground Penetrating Radar Applications. Remote Sens. 2014, 6, 5151-5163. https://doi.org/10.3390/rs6065151
Lei W, Shi R, Dong J, Shi Y. A Multi-Scale Weighted Back Projection Imaging Technique for Ground Penetrating Radar Applications. Remote Sensing. 2014; 6(6):5151-5163. https://doi.org/10.3390/rs6065151
Chicago/Turabian StyleLei, Wentai, Ronghua Shi, Jian Dong, and Yujia Shi. 2014. "A Multi-Scale Weighted Back Projection Imaging Technique for Ground Penetrating Radar Applications" Remote Sensing 6, no. 6: 5151-5163. https://doi.org/10.3390/rs6065151