Detecting and filtering unwanted features from GPR recordings using stationary wavelet transform (CROSBI ID 731007)
Prilog sa skupa u zborniku | prošireni sažetak izlaganja sa skupa
Podaci o odgovornosti
Štifanić, Daniel ; Musulin, Jelena ; Baressi Šegota, Sandi ; Glučina, Matko ; Car, Zlatan
engleski
Detecting and filtering unwanted features from GPR recordings using stationary wavelet transform
Detecting and identifying underground objects without excavation can be demanding, time- consuming, and at the same time, very challenging. Nowadays, with different approach i.e., by utilizing ground-penetrating radar (GPR), underground utilities such as pipes, metals, cables, concrete etc. can be observed and explored from the ground surface. Since such a method is nondestructive, sub-surface surveying has been achieved using electromagnetic radiation. First, a high-frequency electromagnetic wave is emitted into the ground where the signal is reflected, scattered, or refracted from different subsurface structures. Afterwards, the returning signal is received and recorded by the GPR. The received signal can be visually interpreted as an image where the x-axis represents the position of the radar, while the y-axis represents the depth of wave penetration. After the ground has been observed, and recordings acquired, the next step is analyzing and post-processing the data. In real-world applications, GPR recordings can be very noisy due to the presence of different unwanted objects or specific soil layers that can produce interference. Such unwanted features are contained in the same recordings as the valuable features of the observed objects. However, using a mathematical tool called Wavelet Transform (WT), the unwanted features can be partially filtered out.
GPR recordings ; stationary wavelet transform ; filtering
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Podaci o prilogu
32-34.
2021.
objavljeno
Podaci o matičnoj publikaciji
Abstract Book - 6th International Workshop on Data Science / Organiser - Centre of Research Excellence for Data Science and Cooperative Systems Research Unit for Data Science
Lončarić, Sven ; Šmuc, Tomislav
Podaci o skupu
6th International Workshop on Data Science (IWDS 2021)
predavanje
24.11.2021-24.11.2021
Zagreb, Hrvatska