Multi-Feature Based Detection of Landmines Using Ground Penetrating Radar

Park, Kyung-Mi; Park, Suncheol; Kim, Kangwook; Ko, Kwang Hee

doi:10.2528/pier12100405

Cited by 13 publications

(8 citation statements)

References 58 publications

(63 reference statements)

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“…Ground penetrating radar (GPR) has been broadly used in many civilian and military applications in order to nondestructively investigate the subsurface property, i.e., in geological and archeological surveys, for detecting buried objects and locating subsurface cavities, due to its easy usage, quick operation, and low cost [1][2][3][4][5][6][7][8]. It is less destructive and can provide more information than other methods of exploration, i.e., borehole and drilling radar systems.…”

Section: Introductionmentioning

confidence: 99%

Two-Dimensional Microwave Tomographic Algorithm for Radar Imaging Through Multilayered Media

Zhang¹

2014

PIER

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Abstract-The imaging of targets embedded in a planar layered background media has been an important topic in subsurface and urban sensing. In this paper a fast and efficient tomographic algorithm for the imaging of targets embedded in a multilayered media is presented. The imaging algorithm is based on the first-order Born approximation and exploits the spectral multilayered media Green's function. The exploding reflection model is employed and then the Green's function is expanded in the spectral form to facilitate the easy implementation of the imaging algorithm with fast Fourier transform (FFT). The wave propagation effect due to the presence of the layered subsurface media is automatically taken into account in the imaging formulation through the multilayer media Green's function. The linearization of the inversion scheme and employment of FFT make the imaging algorithm suitable in several applications concerning the diagnostics of large probed domain and allow real-time processing. Representative examples are presented to show the effectiveness and efficiency of the proposed algorithm for radar imaging through multilayered media.

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Section: Introductionmentioning

confidence: 99%

Two-Dimensional Microwave Tomographic Algorithm for Radar Imaging Through Multilayered Media

Zhang¹

2014

PIER

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“…In this scenario, the classifier is applied at regular intervals along the A-scan and keypoints at the locations with the largest classifier decision statistics are utilized [6], [7], [11], [28]. The strategies in [8], [10], [20], [35], [36], [39], [45] set = , so that the same number of keypoints are utilized in training and testing.…”

Section: B Strategies For Testingmentioning

confidence: 99%

On Choosing Training and Testing Data for Supervised Algorithms in Ground-Penetrating Radar Data for Buried Threat Detection

Reichman

Collins

Malof

2018

IEEE Trans. Geosci. Remote Sensing

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Abstract-Ground penetrating radar (GPR) is one of the most popular and successful sensing modalities that has been investigated for landmine and subsurface threat detection. Many of the detection algorithms applied to this task are supervised and therefore require labeled examples of target and non-target data for training. Training data most often consists of 2-dimensional images (or patches) of GPR data, from which features are extracted, and provided to the classifier during training and testing. Identifying desirable training and testing locations to extract patches, which we term "keypoints", is well established in the literature. In contrast however, a large variety of strategies have been proposed regarding keypoint utilization (e.g., how many of the identified keypoints should be used at targets, or non-target, locations). Given the variety keypoint utilization strategies that are available, it is very unclear (i) which strategies are best, or (ii) whether the choice of strategy has a large impact on classifier performance. We address these questions by presenting a taxonomy of existing utilization strategies, and then evaluating their effectiveness on a large dataset using many different classifiers and features. We analyze the results and propose a new strategy, called PatchSelect, which outperforms other strategies across all experiments. Index Terms-training, ground penetrating radar, landmine detection I. INTRODUCTIONA popular approach for detecting buried threats, such as landmines and other explosive hazards, is the use of remote sensing technologies. One of the most successful modalities for remote sensing of buried threats is the ground penetrating radar (GPR) [1]- [5]. The typical GPR consists of an array of antennas that are directed toward the ground. An individual GPR antenna operates by emitting a radar signal towards the ground and then measuring the energy that is reflected back. The result of this sensing process is a time-series of energy measurements for the given antenna, referred to as an A-scan [6].In the context of buried threat detection (BTD), GPR sensors collect A-scans at regular spatial intervals as they move across the surface of the ground (e.g., on the front of a vehicle as it drives forward). The resulting A-scans, each collected at a different spatial location, can then be concatenated to form images of the subsurface, termed B-scans [1], [2], [7]. B-scans have one spatial axis, and one temporal axis. The signals returned from buried threats typically exhibit characteristic hyperbolic patterns in the B-scans, which can be leveraged for detection [6], [8]-[10]. Figure 1 shows several examples of Bscans collected over buried threats.Although it is possible to manually identify buried threats in GPR data, a great deal of published research has focused on automating this process with computer algorithms that provide a confidence of buried threat presence at each spatial location [4] A typical processing pipeline for supervised detection algorithms begins with a "prescreening"...

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“…The characteristics of each landmine is summarized in Table 4 [22]. Those landmines were considered at different depths.…”

Section: Case Of Real Landmines Simulationsmentioning

confidence: 99%

An Emi Inversing Problem for Landmine Characterization Based on Improved Particle Swarm Optimization and Finite Element Analysis

Matriche¹,

Féliachi²,

Zaoui³

et al. 2013

PIER B

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Abstract-This paper discusses the characterization of landmine by using the electromagnetic induction technique (EMI). The proposed approach is based on the identification of the physical and geometrical properties of a landmine, from the sensor response. But in such an identification, the inverse problem is unavoidable. At first, we begin by simulating the landmine signature by solving a direct problem using the finite element method which constitutes the direct model. After that, we determine the landmine characteristics by using an inverse model based on a cost function optimization. This model is based on an iterative process which coupling finite element analysis and Particles Swarm Optimization (PSO). In this step, we apply two PSO techniques: the Standard PSO (SPSO) and the Improved PSO (IPSO), and discuss the problem of local minima of the cost function. The proposed iterative model is applied to determine the conductivity, geometry, and depth of metallic landmine from its signature measured by EMI. The numerical solution gives good results for the identification of landmine.

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Multi-Feature Based Detection of Landmines Using Ground Penetrating Radar

Cited by 13 publications

References 58 publications

Two-Dimensional Microwave Tomographic Algorithm for Radar Imaging Through Multilayered Media

Two-Dimensional Microwave Tomographic Algorithm for Radar Imaging Through Multilayered Media

On Choosing Training and Testing Data for Supervised Algorithms in Ground-Penetrating Radar Data for Buried Threat Detection

An Emi Inversing Problem for Landmine Characterization Based on Improved Particle Swarm Optimization and Finite Element Analysis

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