The use of vehicle- or air-borne Ground Penetrating Synthetic Aperture Radar (GPSAR) to quickly detect landmines over large areas is becoming a trend. However, producing too many false alarms in GPSAR landmine detecti...The use of vehicle- or air-borne Ground Penetrating Synthetic Aperture Radar (GPSAR) to quickly detect landmines over large areas is becoming a trend. However, producing too many false alarms in GPSAR landmine detection is a major challenge in practical applications of GPSAR. Support Vector Machine (SVM), employing structural risk minimization theory, does not need large amounts of training data, which makes it suitable for solving the landmine detection problem. In this paper, a novel SVM with a hypersphere instead of a hyperplane classification boundary is proposed for landmine detection in GPSAR. The HyperSphere-SVM (HS-SVM) can be trained with both landmine and clutter data, or with landmine data only, which are called the two-class HS-SVM and the one-class HS-SVM, respectively. The HS-SVM has better generalization capability than the traditional HyperPlane-SVM (HP-SVM) with respect to varying operating conditions. Quantitative comparisons have been made using real data collected with the rail-GPSAR landmine detection system, which show that both the two-class and the one-class HS-SVMs have better detection performance than the HP-SVM.展开更多
Ground Penetrating Radar method was used in detecting the flaws of underground pipeline. The GPR layer disturbing image was summarized by using a rational method in fieldwork and the in-door interpretation of data. Th...Ground Penetrating Radar method was used in detecting the flaws of underground pipeline. The GPR layer disturbing image was summarized by using a rational method in fieldwork and the in-door interpretation of data. The mark radar images of disturbance of slight, middle, and strong were obtained. The result shows that the radar method can not only determine the position of the concrete pipeline underground, but it can detect the laying quality of pipeline as well.展开更多
Ground Penetrating Radar(GPR) is an effective Non-Destructive Testing(NDT) technique for highway pavement surveys, which is able to acquire continuous pavement data compared with traditional core drilling method. In t...Ground Penetrating Radar(GPR) is an effective Non-Destructive Testing(NDT) technique for highway pavement surveys, which is able to acquire continuous pavement data compared with traditional core drilling method. In this study, we proposed an accurate and efficient method to estimate the thickness of each pavement layer using an air-coupled GPR system. For this work, the main difficulties are estimating each pavement layer's time delay and dielectric constant. We first give the basic signal model for pavement evaluation, and then present an Intrinsic Mode Functions(IMFs) product detector to determine each pavement layer's time delay. This method is based on Empirical Mode Decomposition(EMD), which is an adaptive signal decomposition procedure and proved to be suitable for suppressing noises in GPR signal. The dielectric constant was determined by metal reflection measurement. The laboratory and highway experiments illustrate that the proposed thickness estimation method yields reasonable result, thus meets the requirements of practical highway pavement survey with massive GPR data.展开更多
文摘The use of vehicle- or air-borne Ground Penetrating Synthetic Aperture Radar (GPSAR) to quickly detect landmines over large areas is becoming a trend. However, producing too many false alarms in GPSAR landmine detection is a major challenge in practical applications of GPSAR. Support Vector Machine (SVM), employing structural risk minimization theory, does not need large amounts of training data, which makes it suitable for solving the landmine detection problem. In this paper, a novel SVM with a hypersphere instead of a hyperplane classification boundary is proposed for landmine detection in GPSAR. The HyperSphere-SVM (HS-SVM) can be trained with both landmine and clutter data, or with landmine data only, which are called the two-class HS-SVM and the one-class HS-SVM, respectively. The HS-SVM has better generalization capability than the traditional HyperPlane-SVM (HP-SVM) with respect to varying operating conditions. Quantitative comparisons have been made using real data collected with the rail-GPSAR landmine detection system, which show that both the two-class and the one-class HS-SVMs have better detection performance than the HP-SVM.
文摘Ground Penetrating Radar method was used in detecting the flaws of underground pipeline. The GPR layer disturbing image was summarized by using a rational method in fieldwork and the in-door interpretation of data. The mark radar images of disturbance of slight, middle, and strong were obtained. The result shows that the radar method can not only determine the position of the concrete pipeline underground, but it can detect the laying quality of pipeline as well.
基金Supported by the 863 National High Technology Research and Development Program(No.2012AA121901)
文摘Ground Penetrating Radar(GPR) is an effective Non-Destructive Testing(NDT) technique for highway pavement surveys, which is able to acquire continuous pavement data compared with traditional core drilling method. In this study, we proposed an accurate and efficient method to estimate the thickness of each pavement layer using an air-coupled GPR system. For this work, the main difficulties are estimating each pavement layer's time delay and dielectric constant. We first give the basic signal model for pavement evaluation, and then present an Intrinsic Mode Functions(IMFs) product detector to determine each pavement layer's time delay. This method is based on Empirical Mode Decomposition(EMD), which is an adaptive signal decomposition procedure and proved to be suitable for suppressing noises in GPR signal. The dielectric constant was determined by metal reflection measurement. The laboratory and highway experiments illustrate that the proposed thickness estimation method yields reasonable result, thus meets the requirements of practical highway pavement survey with massive GPR data.
