Underground Object Classification for Urban Roads Using Instantaneous Phase Analysis of Ground-Penetrating Radar (GPR) Data
"> Figure 1
<p>Working principles of ground-penetrating radar (GPR).</p> "> Figure 2
<p>Schematic flow diagram of the proposed underground object classification technique.</p> "> Figure 3
<p>Background filtering to enhance underground object visualization. Background signals are eliminated using a basis pursuit approach so that only underground object reflection signals remain.</p> "> Figure 4
<p>Phases of reflected waves from underground objects. The direct waves and the reflected waves are (<b>a</b>) in phase for cavities and (<b>b</b>) out of phase for high-permittivity objects, e.g., metallic pipes.</p> "> Figure 5
<p>Method of two-dimensional (2D) simulation for validation of the proposed technique.</p> "> Figure 6
<p>Underground classification for the pristine model: (<b>a</b>) raw GPR data and (<b>b</b>) processed GPR data.</p> "> Figure 7
<p>Underground object classification results where an underground (<b>a</b>) cavity and (<b>b</b>) pipe are present in the model. Red circles represent the locations of the underground objects. The reflections from the underground objects are better visualized in (<b>c</b>,<b>d</b>), where the background signals have been removed. The instantaneous phase values (<b>e</b>,<b>f</b>) show that the direct wave (blue box, corresponding to the blue circles in (<b>a</b>,<b>b</b>)) and the underground object reflection (red box, corresponding to the red circles in (<b>a</b>,<b>b</b>)) are in-phase for the cavity and out-of-phase for the pipe. This becomes easier to distinguish after binarization, as shown in (<b>g</b>,<b>h</b>). Both phase change ratios are positive in the case of a cavity reflection (<b>g</b>), whereas the change ratio of the pipe reflection is negative (<b>h</b>).</p> "> Figure 8
<p>Underground visualization results with different cavity sizes: (<b>a</b>,<b>b</b>) raw GPR images and (<b>c</b>,<b>d</b>) processed GPR images with 2 and 25 cm diameter cavities, respectively. The red circles indicate the size and location of each modeled cavity.</p> "> Figure 9
<p>Underground visualization results with different soil permittivity: (<b>a</b>,<b>b</b>) raw GPR images and (<b>c</b>,<b>d</b>) processed GPR images of a cavity with soil permittivity <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mi mathvariant="normal">s</mi> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mi mathvariant="normal">s</mi> </msub> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, respectively. (<b>e</b>,<b>f</b>) raw GPR images and (<b>g</b>,<b>h</b>) processed GPR images of a pipe with soil permittivity <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mi mathvariant="normal">s</mi> </msub> <mo>=</mo> <mn>2</mn> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <msub> <mi>ε</mi> <mi mathvariant="normal">s</mi> </msub> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math>, respectively. The red circles indicate the size and location of each underground object.</p> "> Figure 10
<p>Underground visualization results with multiple underground objects: (<b>a</b>) an underground model with a cavity and a pipe, (<b>b</b>) raw GPR image and (<b>c</b>) background filtered GPR image. (<b>d</b>) and (<b>e</b>) presents the instantaneous phase values at the cavity and the pipe location, respectively.</p> "> Figure 11
<p>Underground visualization results for complex underground compositions: A sand layer is added (<b>a</b>) below and (<b>b</b>) above a cavity. (<b>c,d</b>) raw GPR images and (<b>e,f</b>) background filtered GPR images for each model. (<b>g</b>,<b>h</b>) presents the binarization of the phase signals at the cavity locations for each model, both showing in-phase reflections.</p> "> Figure 11 Cont.
<p>Underground visualization results for complex underground compositions: A sand layer is added (<b>a</b>) below and (<b>b</b>) above a cavity. (<b>c,d</b>) raw GPR images and (<b>e,f</b>) background filtered GPR images for each model. (<b>g</b>,<b>h</b>) presents the binarization of the phase signals at the cavity locations for each model, both showing in-phase reflections.</p> "> Figure 12
<p>Field testing on urban roads in Seoul, South Korea: (<b>a</b>) location of the region inspected and (<b>b</b>) field data collection system.</p> "> Figure 13
<p>Composition of the subsurface and the four analyzed regions.</p> "> Figure 14
<p>Results of subsurface analysis in the pristine region with (<b>a</b>) raw GPR data and (<b>b</b>) data processed using the proposed technique at a width of 1.275 m. The C-scan images on the right correspond to the red lines in (<b>b</b>). No underground objects are observed. (<b>c</b>) Raw phase and (<b>d</b>) binarized phase information for the direct wave (blue circle in (<b>b</b>)) are also shown.</p> "> Figure 14 Cont.
<p>Results of subsurface analysis in the pristine region with (<b>a</b>) raw GPR data and (<b>b</b>) data processed using the proposed technique at a width of 1.275 m. The C-scan images on the right correspond to the red lines in (<b>b</b>). No underground objects are observed. (<b>c</b>) Raw phase and (<b>d</b>) binarized phase information for the direct wave (blue circle in (<b>b</b>)) are also shown.</p> "> Figure 15
<p>Results of underground classification for a cavity in Region 1 with (<b>a</b>) raw GPR data and (<b>b</b>) data processed using the proposed technique at a width of 0.375 m. The cavity is clearly visualized in the C-scan images on the right, which correspond to the red lines in (<b>b</b>). (<b>c</b>) Raw phase and (<b>d</b>) binarized phase information for the cavity reflection (red circle in (<b>b</b>)) show a positive phase change ratio.</p> "> Figure 16
<p>Results of underground classification for a pipe in Region 2 with (<b>a</b>) raw GPR data and (<b>b</b>) data processed using the proposed technique at a width of 1.2 m. The pipe is clearly visible in the C-scan images on the right, which correspond to the red lines in (<b>b</b>). (<b>c</b>) Raw phase and (<b>d</b>) binarized phase information for the cavity reflection (red circle in (<b>b</b>)) show a negative phase change ratio.</p> "> Figure 17
<p>Results of underground classification of Region 3 gravels with (<b>a</b>) raw GPR data and (<b>b</b>) data processed using the proposed technique at a width of 0.2625 m. The gravels are clearly visible in the C-scan images on the right, which correspond to the red lines in (<b>b</b>). (<b>c</b>) Raw phase and (<b>d</b>) binarized phase information for the gravel reflection (red circle in (<b>b</b>)) show a negative phase change ratio.</p> "> Figure 18
<p>Underground classification results for Regions 1, 2, and 3 with data processed by (<b>a,c,e</b>) the subtraction method and (<b>b</b>,<b>d</b>,<b>f</b>) the proposed technique, respectively. The red boxes in (<b>a</b>,<b>c</b>,<b>e</b>) indicates the uneliminated soil layer boundaries even after applying the subtraction method, while they are successfully eliminated using the proposed technique in (<b>b,d,f</b>).</p> ">
Abstract
:1. Introduction
2. Underground Object Classification Using GPR Data
2.1. Working Principles of GPR
2.2. GPR Signal Analysis for Underground Object Classification
2.2.1. GPR Data Collection
2.2.2. Background Filtering to Enhance Underground Object Visualization
2.2.3. Instantaneous Phase Analysis for Underground Object Classification
2.2.4. Decision Making
3. Simulation Result: Numerical Validation Using GPR Simulation Data
3.1. Simulation Setup
3.2. Classification Results for the Pristine Model
3.3. Classification Results for Models with Underground Objects
3.4. Robustness of the Proposed Technique to Complicated Underground Features
4. Experimental Result: Field Validation Tests Using 3D GPR Data
4.1. Experimental Setup
4.2. Underground Object Classification in the Pristine Region
4.3. Underground Object Classification in the Regions with Underground Objects
5. Discussion
5.1. Underground Object Classification Compared with the Conventional Subtraction Method
5.2. Underground Object Classification Using a Phase Change Ratio
5.3. Limitations of the Proposed Technique
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
References
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Region | Underground Object | Phase Change Ratio | Comparison with the Reference |
---|---|---|---|
Reference | - | +25.4 m−1 | - |
1 | Cavity | +50.9 m−1 | In-phase |
2 | Pipe | −43.7 m−1 | Out-of-phase |
3 | Gravels | −25.4 m−1 | Out-of-phase |
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Park, B.; Kim, J.; Lee, J.; Kang, M.-S.; An, Y.-K. Underground Object Classification for Urban Roads Using Instantaneous Phase Analysis of Ground-Penetrating Radar (GPR) Data. Remote Sens. 2018, 10, 1417. https://doi.org/10.3390/rs10091417
Park B, Kim J, Lee J, Kang M-S, An Y-K. Underground Object Classification for Urban Roads Using Instantaneous Phase Analysis of Ground-Penetrating Radar (GPR) Data. Remote Sensing. 2018; 10(9):1417. https://doi.org/10.3390/rs10091417
Chicago/Turabian StylePark, Byeongjin, Jeongguk Kim, Jaesun Lee, Man-Sung Kang, and Yun-Kyu An. 2018. "Underground Object Classification for Urban Roads Using Instantaneous Phase Analysis of Ground-Penetrating Radar (GPR) Data" Remote Sensing 10, no. 9: 1417. https://doi.org/10.3390/rs10091417