Volume 10 Issue 1
Feb.  2021
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Article Contents
XUE Cewen, FENG Xuan, LI Xiaotian, et al. Multi-polarization data fusion analysis of full-polarimetric ground penetrating radar[J]. Journal of Radars, 2021, 10(1): 74–85. doi: 10.12000/JR20104
Citation: XUE Cewen, FENG Xuan, LI Xiaotian, et al. Multi-polarization data fusion analysis of full-polarimetric ground penetrating radar[J]. Journal of Radars, 2021, 10(1): 74–85. doi: 10.12000/JR20104

Multi-polarization Data Fusion Analysis of Full-Polarimetric Ground Penetrating Radar

DOI: 10.12000/JR20104
Funds:  The National Key Research and Development Program of China (2018YFC1503705), The Science and Technology on Near-Surface Detection Laboratory (6142414180911), The Fundamental Research Funds for the Central Universities (20130061110061), The Technology Development Program of Jilin Province (20180101091JC)
More Information
  • Corresponding author: FENG Xuan, fengxuan@jlu.edu.cn
  • Received Date: 2020-07-21
  • Rev Recd Date: 2020-09-27
  • Available Online: 2020-10-12
  • Publish Date: 2021-02-25
  • Full-Polarimetric Ground Penetrating Radar (FP-GPR), compared to traditional single-polarimetric GPR, can obtain more comprehensive polarization data (such as VV, HH, and VH) for the same target. To ensure a more comprehensive targets’ image identification, data fusion technology is applied to FP-GPR so as to combine the polarization information of three different polarization modes. However, weighted average fusion is usually employed in FP-GPR data fusion, since it masks the advantages of full polarization and is unable to simultaneously adapt to different target scattering mechanisms. Based on Principal Component Analysis (PCA), Laplacian Pyramid (LP), and multi-scale Wavelet Transform (WT), this research proposes three FP-GPR data fusion methods. To check the reliability of several data fusion methods, we obtained FP-GPR data representing three different target scattering mechanisms in the laboratory and, then, compared the weighted average fusion method with the other three methods using instantaneous amplitude and gradient. The result shows that the three methods were better than the weighted average fusion and that they can be adapted to different target scattering mechanisms. However, PCA was used to fuse the unknown target scattering mechanisms. Finally, PCA fusion is applied to actual ice fracture data imaging, as it produces a better fusion effect than that of weighted average fusion.

     

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