Automatic Analysis of GPR Images: A Pattern-Recognition Approach

Pasolli, Edoardo; Melgani, Farid; Donelli, Massimo

doi:10.1109/tgrs.2009.2012701

Cited by 218 publications

(95 citation statements)

References 22 publications

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“…To overcome speckles issue, we conducted a radargram noise reduction process to detect clear subsurface discontinuity with radon transformation and noise filtering techniques. First proposed in the early 20th century, the radon (tau-p) transformation, which can reconstruct the shape and characteristics of the original object from projected data from various directions, has become an efficient tool for migrating the error of GPR data (Pasolli et al, 2009;Wang and Oristaglio, 2000), especially for linear structure analysis. Since the mentioned advantages of radon transformation accommodated our objective of subsurface layer detection, we projected the original radargram to radon (tau-p) domain and filtered signals in a -10-10-degree range in the tau-p domain ( Figure 5).…”

Section: Pre-processingmentioning

confidence: 99%

The Development of 3d Sub-Surface Mapping Scheme and Its Application to Martian Lobate Debris Aprons

Baik

Kim

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:The Shallow Subsurface Radar (SHARAD), a sounding radar equipped on the Mars Reconnaissance Orbiter (MRO), has produced highly valuable information about the Martian subsurface. In particular, the complicated substructures of Mars such as polar deposit, pedestal crater and the other geomorphic features involving possible subsurface ice body has been successfully investigated by SHARAD. In this study, we established a 3D subsurface mapping strategy employing the multiple SHARAD profiles. A number of interpretation components of SHARAD signals were integrated into a subsurface mapping scheme using radargram information and topographic data, then applied over a few mid latitude Lobate Debris Aprons (LDAs). From the identified subsurface layers of LDA, and the GIS data base incorporating the other interpretation outcomes, we are expecting to trace the origin of LDAs. Also, the subsurface mapping scheme developed in this study will be further applied to other interesting Martian geological features such as inter crater structures, aeolian deposits and fluvial sediments. To achieve higher precision sub-surface mapping, the clutter simulation employing the high resolution topographic data and the upgraded clustering algorithms assuming multiple sub-surface layers will be also developed.

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Section: Pre-processingmentioning

confidence: 99%

The Development of 3d Sub-Surface Mapping Scheme and Its Application to Martian Lobate Debris Aprons

Baik

Kim

2017

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…InfraStructs [36] suggest future applications for interaction, using terahertz imaging. While research into the object detection and material recognition has been undertaken on buried objects with GPR images [6,23]. Regardless of the approach, these sensing methods are complex and costly, let alone the size and power requirements.…”

Section: Materials Classificationmentioning

confidence: 99%

RadarCat

Yeo

Flamich

Schrempf

et al. 2016

Proceedings of the 29th Annual Symposium on User Interface Software and Technology

128

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In RadarCat we present a small, versatile radar-based system for material and object classification which enables new forms of everyday proximate interaction with digital devices. We demonstrate that we can train and classify different types of materials and objects which we can then recognize in real time. Based on established research designs, we report on the results of three studies, first with 26 materials (including complex composite objects), next with 16 transparent materials (with different thickness and varying dyes) and finally 10 body parts from 6 participants. Both leave one-out and 10-fold cross-validation demonstrate that our approach of classification of radar signals using random forest classifier is robust and accurate. We further demonstrate four working examples including a physical object dictionary, painting and photo editing application, body shortcuts and automatic refill based on RadarCat. We conclude with a discussion of our results, limitations and outline future directions.

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“…Methods for automatic recognition of these linear objects in GPR images can be classified mainly into three types: machine learning based methods, clustering based methods, and Hough transform (HT) based methods [16]. Machine learning methods usually require a training process, and the accuracy of recognition results depends on the quality and quantity of the training data [17][18][19], which limits its application. Traditional clustering methods usually require prior knowledge on the number of the clusters, and are not able to detect noisy and interfering hyperbolas.…”

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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Automatic Analysis of GPR Images: A Pattern-Recognition Approach

Cited by 218 publications

References 22 publications

The Development of 3d Sub-Surface Mapping Scheme and Its Application to Martian Lobate Debris Aprons

The Development of 3d Sub-Surface Mapping Scheme and Its Application to Martian Lobate Debris Aprons

RadarCat

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

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