Critical Analysis of Background Subtraction Techniques on Real GPR Data

Ground penetrating radar (GPR) is used to detect the underground buried objects for civil as well as defence applications under varying conditions of soil moisture content. The capability of detection depends upon soil moisture, target characteristics and subsurface characteristics, which are mainly responsible for contaminating the GPR images with clutter. Researchers earlier have used averaging, mean, median, Eigen values, etc. for subtracting the background from GPR images. To analyse the background subtraction or clutter reduction problems, in this paper, we have experimentally reviewed background subtraction techniques with or without target conditions to enhance the target detection under variable soil moisture content. Indigenously developed GPR has been used to collect the data for different soil conditions and several background subtraction signal processing techniques were critically reviewed like, mean, median, singular value decomposition (SVD), principal component analysis (PCA), independent component analysis (ICA) and training methods. The signal to clutter ratio (SCR) measurement has been used for performance evaluation of each technique. The relative merits and demerits of each technique has also been analysed. The background subtraction techniques have been appliedto experimental GPR data and it is observed that in comparison of mean, SVD, median, ICA, PCA, the training method shows the highest SCR with buried target. Finally, this review helps to select the comparatively better background subtraction technique to enhance the detection capability in GPR

Inverse Transmission Line Modeling of GPR for Landmine Detection and Subsurface Parameter Estimation

This paper presents detection of plastic landmines and characterization of subsurface and the target using ground penetrating radar (GPR) measurements. The inverse transmission line (TL) modeling approach is used to analyze the characteristics of practical GPR system operating above non-uniform ground. The modeling approach considers the presence of surface roughness, natural clutter, targets and soil moisture. With this model, the time domain signature of electromagnetic (EM) propagation can be assessed. The inverse TL modeling is applied to estimate the characteristic parameters (such as conductivity, magnetic permeability and permittivity) of the subsurface and buried plastic landmines. TL modeling is applied to obtain the reverse solution of the electromagnetic equations. Different scenarios were considered and test signals of the B-scan data are used to test the effectiveness of the method. Simulations of the real data analysis showed the effectiveness of the model and its application to target detection and characterization of the subsurface. Performance of the modeling is analyzed using receiver operating characteristic (ROC) analysis of the four hypotheses and it has found that 99.5% detection has achieved as the cost of 12% of false alarm. Keywords: Dielectric properties; EM wave propagation; GPR; Parameter estimation; Inverse transmission line

Robots for Humanitarian Demining

More than 100,000,000 anti-personnel mines have been laid in deferent part of the world by terrorists or government forces. The mines are cheapest weapon, built to make horrible injuries, affecting active people, with major falls-off into economic growth. Therefore, after or during a war demining is a big technological problem which needs to address by the governments. All demining activities can be classified mainly in two different ways, military demining and humanitarian demining. Main objective of military demining is to make a quick safe path for troops and may be 80% clearing is enough for them. On the other hand, humanitarian demining target is to clear 100% to ensure the use of lands by people who are not involved in the conflicts for their day-to-day activities including farming. Mainly humanitarian demining has two tasks: detection and removal. Still the use of robots is questionable in this regard. Mainly robots work well for clean and reliable tasks. When the price to performance ratio is too high, they are academic toys. This chapter presents the overview of the available robotic technologies with a depth comparison between them by considering the appropriateness to the local context

An Approach for Predicting the Shape and Size of a Buried Basic Object on Surface Ground Penetrating Radar System

Surface ground-penetrating radar (GPR) is one of the radar technology that is widely used in many applications. It is nondestructive remote sensing method to detect underground buried objects. However, the output target is only hyperbolic representation. This research develops a system to identify a buried object on surface GPR based on decision tree method. GPR data of many basic objects (with circular, triangular, and rectangular cross-section) are classified and extracted to generate data training model as a unique template for each type of basic object. The pattern of object under test will be known by comparing its data with the training data using a decision tree method. A simple powerful algorithm to extract feature parameters of object which is based on linear extrapolation is proposed. The result showed that tested buried basic objects can be correctly predicted and the developed system works properly

Ground Penetrating Radar-based Deterioration Assessment of Bridge Decks

The ASCE report card 2013 rated bridges at a grade of C+, implying their condition is moderate and require immediate attention. Moreover, the Federal Highway Administration reported that it is required to invest more than $20.5 billion each year to eliminate the bridge deficient backlog by 2028. In Canada 2012, more than 50$90 billion are required to replace these bridges. Therefore, government agencies should have an accurate way to inspect and assess the corrosiveness of the bridges under their management. Numerical Amplitude method is one of the most common used methods to interpret Ground Penetrating Radar (GPR) outputs, yet it does not have a fixed and informative numerical scale that is capable of accurately interpreting the condition of bridge decks. To overcome such problem, the present research aims at developing a numerical GPR-based scale with three thresholds and build deterioration models to assess the corrosiveness of bridge decks. Data, for more than 60 different bridge decks, were collected from previous research works and from surveys of bridge decks using a ground-coupled antenna with the frequency of 1.5 GHz. The amplitude values of top reinforcing rebars of each bridge deck were classified into four categories using k-means clustering technique. Statistical analysis was performed on the collected data to check the best-fit probability distribution and to choose the most appropriate parameters that affect thresholds of different categories of corrosion and deterioration. Monte-Carlo simulation technique was used to validate the value of these thresholds. Moreover, a sensitivity analysis was performed to realize the effect of changing the thresholds on the areas of corrosion. The final result of this research is a four-category GPR scale with numerical thresholds that can assess the corrosiveness of bridge decks. The developed scale has been validated using a case study on a newly constructed bridge deck and also by comparing maps created using the developed scale and other methods. The comparison shows sound and promising results that advance the state of the art of GPR output interpretation and analysis. In addition, deterioration models and curves have been developed using Weibull Distribution based on GPR outputs and corrosion areas. The developed new GPR scale and deterioration models will help the decision makers to assess accurately and objectively the corrosiveness of bridge decks. Hence, they will be able to take the right intervention decision for managing these decks

Signal Processing Techniques for Landmine Detection Using Impulse Ground Penetrating Radar (ImGPR)

Landmines and unexploded ordinance (UXO) are laid during a conflict against enemy forces. However, they kill or maim civilians decades after the conflict has ended. There are more than 110 million landmines actively lodged in the globe. Every year more than 26,000 innocent civilians are killed or maimed. Most modern landmines are mainly nonmetallic or plastic, which are difficult to be detected using conventional metal detectors. Detection using hand-held prodding is a slow and expensive process. Impulse Ground Penetrating Radar (ImGPR) is a nondestructive technique capable of detecting shallowly buried nonmetallic anti-personnel (AP) and anti-tank (AT) landmines. In this PhD thesis, ImGPR is considered as a tool to detect landmines and UXO. The presence of strong ground clutter and noise degrade the performance of GPR. Hence, using a GPR sensor is almost impossible without the application of sophisticated signal processing. In electromagnetic wave propagation modeling, a multilayer transmission line technique is applied. It considers different soil types at different moisture levels. Plastic targets of different diameters are buried at different depths. The modeled signal is then used to estimate the ground and buried target parameters. In a parameter estimation procedure, a surface reflection parameter method (SRPM) is applied. Signal processing algorithms are implemented for clutter reduction and decision making purposes. Attention is mainly given to the development of techniques, that are applicable to real-time landmine detection. Advanced techniques are preceded by elementary preprocessing techniques, which are useful for signal correction and noise reduction. Background subtraction techniques based on multilayer modeling, spatial filtering and adaptive background subtraction are implemented. In addition to that, decorrelation and symmetry filtering techniques are also investigated. In the correlated decision fusion framework, local decisions are transmitted to the fusion center so as to compute a global decision. In this case, the concept of confidence information of local decisions is crucial to obtain acceptable detection results. The Bahadur-Lazarsfeld and Chow expansions are used to estimate the joint probability density function of the correlated decisions. Furthermore, a decision fusion based on fuzzy set is implemented. All proposed methods are evaluated using simulated as well as real GPR data measurements of many scenarios. The real data collection campaign took place at the Griesheim old airport and Botanischer Garten, Darmstadt, Germany in July 2011

Microwave NDT&E using open-ended waveguide probe for multilayered structures

Ph. D. Thesis.Microwave NDT&E has been proved to be suitable for inspecting of dielectric structures due to low attenuation in dielectric materials and free-space. However, the microwave responses from multilayered structures are complex as an interrogation of scattering electromagnetic waves among the layers and defects. In many practical applications, electromagnetic analysis based on analytic- and forward structural models cannot be generalised since the defect shape and properties are usually unknown and hidden beneath the surface layer. This research proposes the design and implementation of microwave NDT&E system for inspection of multilayered structures. Standard microwave open-ended rectangular waveguides in X, Ku and K bands (frequency range between 8-26.5 GHz) and vector network analyser (VNA) generating sweep frequency of wideband monochromatic waves have been used to obtain reflection coefficient responses over three types of challenging multilayered samples: (1) corrosion progression under coating, (2) woven carbon fibre reinforced polymer (CFRP) with impact damages, and (3) thermal coated glass fibre reinforced polymer (GFRP) pipe with inner flat-bottom holes. The obtained data are analysed by the selected feature extraction method extracting informative features and verify with the sample parameters (defect parameters). In addition, visualisation methods are utilised to improve the presentation of the defects and material structures resulting in a better interpretation for quantitative evaluation. The contributions of this project are summarised as follows: (1) implementation of microwave NDT&E scanning system using open-ended waveguide with the highest resolution of 0.1mm x 0.1 mm, based on the NDT applications for the three aforementioned samples; (2) corrosion stages of steel corrosion under coating have been successfully characterised by the principal component analysis (PCA) method; (3) A frequency selective based PCA feature has been used to visualise the impact damage at different impact energies with elimination of woven texture influences; (4) PCA and SAR (synthetic aperture radar) tomography together with time-offlight extraction, have been used for detection and quantitative evaluation of flat-bottom hole defects (i.e., location, size and depth). The results conclude that the proposed microwave NDT&E system can be used for detection and evaluation of multilayered structures, which its major contributions are follows. (1) The early stages (0-12month) of steel corrosion undercoating has been successfully characterised by mean of spectral responses from microwave opened rectangular waveguide probe and PCA. (2) The detection of low energy impact damages on CFRP as low as 4 Joules has been archived with microwave opened rectangular waveguide probe raster scan together with SAR imaging and PCA for feature extraction methods. (3) The inner flat-bottom holes beneath the thermal coated GFRP up to 11.5 mm depth has been successfully quantitative evaluated by open-ended waveguide raster scan using PCA and 3-D reconstruction based on SAR tomography techniques. The evaluation includes location, sizing and depth. Nevertheless, the major downside of feature quantities extracted from statistically based methods such as PCA, is it intensely relies on the correlation of the input dataset, and thus hardly link them with the physical parameters of the test sample, in particular, the complex composite architectures. Therefore, there are still challenges of feature extraction and quantitative evaluation to accurately determine the essential parameters from the samples. This can be achieved by a future investigation of multiple features fusion and complementary features.Ministry of Science and Technology of Royal Thai Government and Office of Educational Affairs, the Royal Thai Embass