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Deep-Learning-Based Calibration in Contrast Source Inversion Based Microwave Subsurface Imaging
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DOI：10.1109/LGRS.2022.3169799.png)
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Abstract
En 中文
A deep-learning (DL)-based data calibration technique applied to quantitative microwave inverse-scattering analysis is presented. This technique aims at subsurface inspection for the buried object under a concrete road or soil. The inverse-scattering analysis provides a complex permittivity profile, which is useful for object identification such as air gap or water. Contrast source inversion (CSI) is one of the most promising inverse-scattering methods. This method is capable of avoiding the iterative use of highly computational forward solvers. However, when applied to the measured data, an appropriate calibration capable of converting measured data to simulation data is required. In this work, a DL-based calibration suitable for nonlinear inverse problems is proposed. Its efficiency is experimentally demonstrated using a concrete cylinder containing water with different salinities.
Keywords:
Calibration
Data models
Permittivity
Frequency measurement
Analytical models
Training
Time-domain analysis
Contrast source inversion (CSI)
deep-learning (DL)-based calibration
inverse scattering analysis
microwave quantitative imaging
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