Enhedelihai Nilot

Application of Freeman decomposition to full polarimetric GPR for improving subsurface target classification

Migration technique can reconstruct the subsurface target imaging from record ground penetrating radar (GPR) data. Depending on the reconstructed imaging, we possibly distinguish subsurface target by the geometrical feature. But some different targets have similar reconstructed imaging, which will confuse us. Freeman decomposition is a technique for fitting a three-component scattering mechanism model to polarimetric synthetic aperture radar observations, which is successfully used in the classification of terrain objects. This paper applies the model-based Freeman decomposition to full polarimetric GPR data to improve the subsurface target classification. We use a full polarimetric GPR, which is constructed in laboratory with three types of antenna combinations, HH, VV and VH combinations, to acquire experiment data sets for testing the method. Metallic plate, ball, dihedral and volume scatter with many branches, are buried in the homogenous dry sand under flat ground surface, and three dimensional data sets are acquired above each buried target. After signal processing, we obtained subsurface target imaging by migration and color-coded polarimetric information by Freeman decomposition. Results showed that it can improve the classification capability of GPR for the subsurface target to use both the geometrical feature and polarimetric information. Model-based Freeman decomposition is applied to full polarimetric GPR data.Both geometrical feature and polarimetric information are used to improving the classification of subsurface target.Methodology of imaging and polarimetric decomposition of full polarimetric GPR data is introduced.

IMF-Slices for GPR Data Processing Using Variational Mode Decomposition Method

Using traditional time-frequency analysis methods, it is possible to delineate the time-frequency structures of ground-penetrating radar (GPR) data. A series of applications based on time-frequency analysis were proposed for the GPR data processing and imaging. With respect to signal processing, GPR data are typically non-stationary, which limits the applications of these methods moving forward. Empirical mode decomposition (EMD) provides alternative solutions with a fresh perspective. With EMD, GPR data are decomposed into a set of sub-components, i.e., the intrinsic mode functions (IMFs). However, the mode-mixing effect may also bring some negatives. To utilize the IMFs’ benefits, and avoid the negatives of the EMD, we introduce a new decomposition scheme termed variational mode decomposition (VMD) for GPR data processing for imaging. Based on the decomposition results of the VMD, we propose a new method which we refer as “the IMF-slice”. In the proposed method, the IMFs are generated by the VMD trace by trace, and then each IMF is sorted and recorded into different profiles (i.e., the IMF-slices) according to its center frequency. Using IMF-slices, the GPR data can be divided into several IMF-slices, each of which delineates a main vibration mode, and some subsurface layers and geophysical events can be identified more clearly. The effectiveness of the proposed method is tested using synthetic benchmark signals, laboratory data and the field dataset.

Full-polarimetric GPR system for underground targets measurement

A conventional GPR system includes PC, network analyzer, rectangular coordinates robot and a single antenna for transmission and reception, resulting in a response only to Co-Polarization signal. However, we expect that more polarimetric information can be obtained. So we developed a full-polarimetric GPR system including PC, network analyzer, position controller, switch driver and polarimetric antenna array. This antenna array can obtain CMP multi-offset data gather directly. At every measurement position, the total of three signals was collected not only in Co-Polarimetric mode but also in Cross-Polarimetric mode. Two groups of experiments have been presented. The first group is concerned with a metal dihedral which is made of two orthogonal conducting plates and a metal trihedral as the targets. The result of this experiment shows that the surface morphology of the target characteristics, the relative position and attitude have a certain influence on the measurement results. The second group is using a metal trihedral as the target. The results of experiment are shown in this presentation are consistent with the theoretical values and helps us to identify target attributes such as direction.

Radar polarimetry analysis applied to fully polarimetric Ground Penetrating Radar

Polarimetric decomposition techniques have been applied in remote sensing in the area of air-space-borne radar and have achieved much progress in recent years. However, very few apply these polarimetric decomposition techniques to the Ground Penetrating Radar (GPR).We currently apply GPR data sets to characterize and classify the subsurface targets using Pauli decomposition method. The Pauli decomposition method provided important radar polarimetry information of subsurface targets, and the Pauli decomposition method made a significant contribution to understanding the scattering mechanisms from different subsurface targets with different properties. Analyzing polarimetric attributes of subsurface targets provides facilitates for classifying subsurface targets. Because some methods only can identify the approximate outline of subsurface targets, but can not classify the targets such as imaging technique that can only identify the outline of subsurface targets, but can not classify targets. So we apply Pauli decomposition for classifying subsurface targets and this analysis result is relatively good. The decomposition technique plays a key role for classifying subsurface targets such as metal surface plate, dihedral, metal ball and other subsurface targets. This paper mainly applies Pauli decomposition method to recognize subsurface metal surface plate, subsurface dihedral, subsurface metal ball, subsurface metal bucket, and subsurface chaotic scattering target. This decomposition technique provides valuable information for the study of properties of the subsurface targets.

Full polarimetric GPR data decomposition and imaging

Polarization is a property of electromagnetic wave that generally refers to the orientation of the electric field vector, which can be used to characterize target properties by polarimetric radar. Polarimetric decomposition methods have been common in the terrain and land-use classification based on plolarimetric synthetic aperture radar data. However, the technique has been less common in the ground penetrating radar (GPR) community. In this paper, we introduce the polarimetric decomposition technology into the full polarimetric GPR data analysis. Using the polarimetric decomposition, we extract the polarimetric information of subsurface targets. Also migration can be used to the full polarimetric GPR data, and target imaging can be obtained. Laboratory experiment results show that we can improve the classification of subsurface targets by combing the polarimetric information and imaging.

Design and performance of Full-polarimetric airborne GPR testing system

Airborne ground penetrating radar (GPR) is a suitable tool to perform cost-effective surveys of the underground of a large possibly non-accessible areas. And It is concluded that airborne GPR will receive more attention in the future. So we have developed a L-band Full-polarimetric Step-Frequency GPR acquisition system, which consists of a GPS receiver, the Vivaldi antenna, a signal amplifier and a vector network analyzer (VNA) under the control of a PC unit. The main objective of our work is to conduct some experiments to test the feasibility of this airborne testing system.