On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods

Safont, Gonzalo; Salazar, Addisson; Rodríguez, Alberto; Vergara, Luis

doi:10.3390/rs6087546

Cited by 32 publications

(17 citation statements)

References 21 publications

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“…Other noise reduction method such as wavelet method [43], deconvolution method [44] and K distribution method [45] may also increase the signal-noise ratio of the GPR data and improve the performance of recognition. In addition, an algorithm to recover missing traces is also helpful to improve the completeness of reflection signals when some traces were missed in radargrams [46].…”

Section: Development Of Noise Reduction Methods For Heterogeneous Soimentioning

confidence: 99%

Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Cui

Guo

et al. 2016

Remote Sensing

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Abstract:As a nondestructive geophysical tool, Ground penetrating radar (GPR) has been applied in tree root study in recent years. With increasing amounts of GPR data collected for roots, it is imperative to develop an efficient automatic recognition of roots in GPR images. However, few works have been completed on this topic because of the complexity in root recognition problem. Based on GPR datasets from both controlled and in situ experiments, the randomized Hough transform (RHT) algorithm was evaluated in root object recognition for different center frequencies (400 MHz, 900 MHz, and 2000 MHz) in this paper. Reasonable accuracy was obtained (both a high recognition rate and a low false alarm rate) in these datasets, which shows it is feasible to apply the RHT algorithm for root recognition. Furthermore, we evaluated the influence of root and soil factors on the recognition. We found that the performance of RHT algorithm is mainly affected by root interval length, root orientation, and clutter noise of soil. The recognition results by RHT could be applied for large scale root system distribution study in belowground ecology. Further studies should be conducted to reduce clutter noise and improve the recognition of the complex root reflections.

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Section: Development Of Noise Reduction Methods For Heterogeneous Soimentioning

confidence: 99%

Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Cui

Guo

et al. 2016

Remote Sensing

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“…It is obtained by sampling, over a fixed time window, the electromagnetic pulses transmitted by GPR and then reflected by dielectric discontinuities in the studied structure. A collection of these traces (A-Scans) is called a B-Scan [18]. It is obtained by moving the antenna along a line and taking GPR data at different positions.…”

Section: Data Acquisitionmentioning

confidence: 99%

Mapping of Sewer Lines Using GPR: A Case Study in Tunisia

Ghozzi

Lahouar

Besbes

et al. 2018

Data

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Many infrastructure enhancement projects require underground utility mapping before starting any excavation processes, especially in urban areas. In fact, mapping of an area provides a general overview of the infrastructure above and underground. This mapping can be done by Ground Penetrating Radar (GPR), which is commonly used as a Non-Destructive Testing (NDT) technique that allows, among others, the detection and localization of buried utilities without any damage to the surface. This paper presents the results of mapping sewer lines in an urban region of the city of Tunis, Tunisia, using GPR. In this study a ground-coupled GPR system with a frequency of 400MHz was used to detect and locate the sewer lines in the study area. In order to reduce the amount of collected data over the large study area, some subareas were selected where GPR testing was conducted and data were analyzed. Then, sewer lines were interpolated over the whole area using the GPR results and the locations of manholes visible from the surface. It was shown that using this technique mapping of the pipes in the entire area can be achieved. All data presented in this study were collected by the Office of Topography and Cadastre (OTC) in Tunisia.

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“…some sampling values may not be measured and collected. In order to recover these incomplete data with interpolation methods [12][13], the following steps are applied:…”

Section: Proposed Nonstandard Mean Interpolation Methodsmentioning

confidence: 99%

Interpolation methods for recovering the sampling values of GRP data

SAMET¹

2017

Communications Faculty of Science University of Ankara

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Ground Penetrating Radar (GPR) is widely used to acquire the data from near surface depth. The acquired GPR data allow the users to investigate and examine the underground structures (anomalies) easily, quickly and accurately without any excavation. In GPR studies, data collection parameters such as the profile interval and step size, which can be controlled by users, play an important role in the identification of underground structures. But search area properties such as uneven surface, the presence of archaeological and other obstacles cannot be controlled by users. The obtained accuracy depends on the completeness and resolution of acquired GPR data. Due to some research area properties the data acquired from the search area may become incomplete and inadequate. Before analyzing, visualization and interpretation of the underground structures, the incomplete GPR data should be recovered. In this paper, nonstandard interpolation method are proposed for completing the missing data. The proposed methods were implemented on the real GPR data acquired from the test area. The obtained results showed that the similarity of the produced data as quite closer to the original data.

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On Recovering Missing Ground Penetrating Radar Traces by Statistical Interpolation Methods

Cited by 32 publications

References 21 publications

Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Tree Root Automatic Recognition in Ground Penetrating Radar Profiles Based on Randomized Hough Transform

Mapping of Sewer Lines Using GPR: A Case Study in Tunisia

Interpolation methods for recovering the sampling values of GRP data

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