Buried pipe localization using an iterative geometric clustering on GPR data

Janning, Ruth; Busche, Andre; Horváth, Tomáš; Schmidt-Thieme, Lars

doi:10.1007/s10462-013-9410-2

Cited by 25 publications

(19 citation statements)

References 21 publications

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“…Traditional clustering methods usually require prior knowledge on the number of the clusters, and are not able to detect noisy and interfering hyperbolas. Janning et al [20,21] developed an algorithm named GamRec to handle these problems. However, it was only tested in synthetic radargrams, its application accuracy in practical radargrams is still unknown.…”

Section: Introductionmentioning

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: Introductionmentioning

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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“…One of the main strategies used in the interpretation of GPR images is given by the identification and recognition of hyperbolae. We can usually estimate both the spatial position and the depth of the buried object registered in the GPR images [Janning et al (2014)]. At first, no knowledge about how many hyperbolae exist and where they are in a GPR image.…”

Section: Gpr Interpretationsmentioning

confidence: 99%

“…This happens since the GPR signal has a complex waveform and the single reflector may appear as a pair or more reflections. Sometimes this is overlooked, so there is more than one interpretation for the same GPR reflector profiles [Robinson et al (2013);Janning et al (2014)]. …”

Section: Gpr Interpretationsmentioning

confidence: 99%

“…Noise can intersect with the different hyperbolae belonging to objects or be tangent to them [Janning et al (2014)]. This makes difficult the interpretation of objects in GPR images.…”

Section: Gpr Interpretationsmentioning

confidence: 99%

“…The main interest of this thesis is Pipe recognition, where there are included two subjects: (a) pipe localization, and (b) pipe type estimation [Janning et al (2014)]. …”

Section: Robinsonmentioning

confidence: 99%

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Characterization of components of water supply systems from GPR images and tools of intelligent data analysis.

Ayala-Cabrera¹

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Dedico este trabajo doctoral:En primera instancia a Dios.A mis padres, David Ayala y Nirza Cabrera, porque es por ustedes que soy lo que soy. Gracias por brindarme el apoyo y las bases para sacar mis proyectos adelante. Gracias por todo el cariño y por todos los esfuerzos que han y siguen realizando por mí.A mi adorada esposa, Anastasia Tsitsou, gracias por el apoyo incondicional brindado, por todos los momentos sacrificados y especialmente por los bellos momentos a tu lado, por ser mi motivo de inspiración y mis deseos de superación. Gracias por todo el amor incondicional ofrecido. Mil y mil gracias Agapi mou.Finalmente, a mis hermanos Ramier, Pilar, Patricia y Katerine. AgradecimientosEste trabajo ha sido posible gracias al Programa de Ayudas de Investigación y Desarrollo (PAID) de la Universitat Politècnica de València, que han concedido una beca para el autor, en el marco del Programa para la Formación de Personal Investigador (FPI) para el año 2011.Quiero agradecer a mis directores Rafael Pérez García, Joaquín Izquierdo y Manuel Herrera por creer en este proyecto, por su apoyo, por sus buenos consejos tanto en lo emocional como en lo profesional. Adicionalmente, gracias por ser esa chispa que inició mis caminos en la investigación. A Rafael gracias por su apoyo y constancia y por llevarme por ese tan inexplorado camino de los métodos no destructivos. A Joaquín mis agradecimientos por su entrega y su dedicación, su apoyo y comprensión, gracias por esos inesperados artículos a publicar que medían mi capacidad de reacción. Gracias por toda la dedicación a los incomprensibles momentos de mi creatividad.A Manuel, gracias por todo su apoyo y amistad, por incentivarme a publicar y así compartir mis ideas, gracias por ser ese amigo y colega cómplice en el desarrollo de mi trabajo y por sus invaluables consejos. De igual manera quiero hacer extensivo este agradecimiento a todos los integrantes de FluIng, especialmente a Joanna, Eva, Enrique e Idel.Quiero agradecer a la National Technical University of Athens de Grecia, específicamente al grupo de investigación Microwave and Fiber Optics Laboratory, por acogerme en mi periodo de estancia. Particularmente, quiero agradecer a Manthos Bimpas por su apoyo, amistad y buenos consejos en el ámbito profesional, además de hacer posible el desarrollo de esta estancia. Manthos, su ejemplo de trabajo bien realizado es motivador, Efcharisto Poli.Mis agradecimientos para el Consorcio AQUARUM por su ayuda en la captura de datos de campo. En especial a los ingenieros Mario Tavera y Halmark Mejía.Quiero manifestar mi enorme gratitud a los ingenieros Beatriz Wills y Guillermo Chica de la Universidad de Antioquia, con quienes siempre he podido contar con sus valiosos consejos y amistad. Así mismo, hacer extensivo mi gratitud a mis excompañeros de Ingedelta, Wilmar, Mauricio, Andrés y Jorge, quienes me apoyaron desde los inicios del doctorado. A los Ingenieros Camilo Cruz y Patricia Torres de la Universidad delValle, gracias por brindarme esa mano amiga.Gracias a mis amigos Paola Nates,...

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Characterizing soil piping networks in loess‐derived soils using ground‐penetrating radar

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Bielders

Lambot

2020

Vadose Zone Journal

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Soil piping remains a relatively unexplored phenomenon despite its substantial impacts on water and sediment transfer at the watershed scale at numerous locations around the world. One of the main limitations regarding the study of this singular process is the characterization of the pipe networks (in terms of number, position, dimensions, and connectivity of pipes). In this context, noninvasive subsurface imaging using ground-penetrating radar (GPR) has proven to be a promising technique. This study used two three-dimensional (3D), high-resolution GPR surveys performed in loess-derived soils to characterize small pipe networks with little prior information about their location. The adopted methodology relies on high spatial resolution scanning, 3D subsurface imaging, and automated detection of reflection hyperbolas using a 200-MHz center-frequency antenna. Two small watersheds affected by piping were investigated at Sippenaeken and Kluisbergen (Belgium). Over the two scanned zones, results revealed significant subsurface continuous patterns. Even though the most obvious patterns corresponded to recent or past anthropic activities (e.g., artificial drainage pipes), validation tests confirmed that the chosen methodology can be used for pipe network characterization because some important continuity patterns were related to soil piping: for instance, a fairly small pipe (approximately 10-15 cm in diameter) was detected and validated for more than 100 m. Nevertheless, the high variability in size, depth, and orientation of the pipes imply that GPR may only be truly efficient when using very high spatial resolution scanning, which limits its application to specific conditions not always met in piped areas.

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Buried pipe localization using an iterative geometric clustering on GPR data

Cited by 25 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

Characterization of components of water supply systems from GPR images and tools of intelligent data analysis.

Characterizing soil piping networks in loess‐derived soils using ground‐penetrating radar

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