A depth-adaptive filtering method for effective GPR tree roots detection in tropical area
This study presents a technique for processing step-frequency continuous-wave (SFCW) ground-penetrating radar (GPR) data to detect tree roots. SFCW GPR is portable and enables precise control of energy levels, balancing depth and resolution tradeoffs. However, the high-frequency components of the tr...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/170753 |
| _version_ | 1811681726513020928 |
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| author | Luo, Wenhao Lee, Yee Hui Mohamed Lokman Mohd Yusof Yucel, Abdulkadir C. |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Luo, Wenhao Lee, Yee Hui Mohamed Lokman Mohd Yusof Yucel, Abdulkadir C. |
| author_sort | Luo, Wenhao |
| collection | NTU |
| description | This study presents a technique for processing step-frequency continuous-wave (SFCW) ground-penetrating radar (GPR) data to detect tree roots. SFCW GPR is portable and enables precise control of energy levels, balancing depth and resolution tradeoffs. However, the high-frequency components of the transmission band suffer from poor penetrating capability and generate noise that interferes with root detection. The proposed time-frequency filtering technique uses a short-time Fourier transform (STFT) to track changes in frequency spectrum density over time. To obtain the filter window, a weighted linear regression (WLR) method is used. By adopting a conversion method that is a variant of the chirp Z transform (CZT), the time-frequency window filters out frequency samples that are not of interest when doing the frequency-to-time domain data conversion. The proposed depth-adaptive filter window can self-adjust to different scenarios, making it independent of soil information, and effectively determines subsurface tree roots. The technique is successfully validated using SFCW GPR data from actual sites in a tropical area with different soil moisture levels, and the 2-D radar map of subsurface root systems is highly improved compared to existing methods. |
| first_indexed | 2024-10-01T03:45:32Z |
| format | Journal Article |
| id | ntu-10356/170753 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:45:32Z |
| publishDate | 2023 |
| record_format | dspace |
| spelling | ntu-10356/1707532023-10-02T05:08:38Z A depth-adaptive filtering method for effective GPR tree roots detection in tropical area Luo, Wenhao Lee, Yee Hui Mohamed Lokman Mohd Yusof Yucel, Abdulkadir C. School of Electrical and Electronic Engineering Engineering::Electrical and electronic engineering Chirp Z Transform Depth-Adaptive This study presents a technique for processing step-frequency continuous-wave (SFCW) ground-penetrating radar (GPR) data to detect tree roots. SFCW GPR is portable and enables precise control of energy levels, balancing depth and resolution tradeoffs. However, the high-frequency components of the transmission band suffer from poor penetrating capability and generate noise that interferes with root detection. The proposed time-frequency filtering technique uses a short-time Fourier transform (STFT) to track changes in frequency spectrum density over time. To obtain the filter window, a weighted linear regression (WLR) method is used. By adopting a conversion method that is a variant of the chirp Z transform (CZT), the time-frequency window filters out frequency samples that are not of interest when doing the frequency-to-time domain data conversion. The proposed depth-adaptive filter window can self-adjust to different scenarios, making it independent of soil information, and effectively determines subsurface tree roots. The technique is successfully validated using SFCW GPR data from actual sites in a tropical area with different soil moisture levels, and the 2-D radar map of subsurface root systems is highly improved compared to existing methods. Ministry of National Development (MND) National Parks Board This work was supported by the Ministry of National Development Research Fund, National Parks Board, Singapore. 2023-10-02T05:08:38Z 2023-10-02T05:08:38Z 2023 Journal Article Luo, W., Lee, Y. H., Mohamed Lokman Mohd Yusof & Yucel, A. C. (2023). A depth-adaptive filtering method for effective GPR tree roots detection in tropical area. IEEE Transactions On Instrumentation and Measurement, 72, 3282654-. https://dx.doi.org/10.1109/TIM.2023.3282654 0018-9456 https://hdl.handle.net/10356/170753 10.1109/TIM.2023.3282654 2-s2.0-85161582713 72 3282654 en IEEE Transactions on Instrumentation and Measurement © 2023 IEEE. All rights reserved. |
| spellingShingle | Engineering::Electrical and electronic engineering Chirp Z Transform Depth-Adaptive Luo, Wenhao Lee, Yee Hui Mohamed Lokman Mohd Yusof Yucel, Abdulkadir C. A depth-adaptive filtering method for effective GPR tree roots detection in tropical area |
| title | A depth-adaptive filtering method for effective GPR tree roots detection in tropical area |
| title_full | A depth-adaptive filtering method for effective GPR tree roots detection in tropical area |
| title_fullStr | A depth-adaptive filtering method for effective GPR tree roots detection in tropical area |
| title_full_unstemmed | A depth-adaptive filtering method for effective GPR tree roots detection in tropical area |
| title_short | A depth-adaptive filtering method for effective GPR tree roots detection in tropical area |
| title_sort | depth adaptive filtering method for effective gpr tree roots detection in tropical area |
| topic | Engineering::Electrical and electronic engineering Chirp Z Transform Depth-Adaptive |
| url | https://hdl.handle.net/10356/170753 |
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