Fast and efficient detection of buried object for GPR image

Chomdee, Pongsathorn; Boonpoonga, Akkarat; Prayote, Akara

doi:10.1109/apcc.2014.7092835

Cited by 10 publications

(3 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, many strategies utilize keypoints at the top 𝐾 energy locations, where 𝐾 is the same for both target and nontarget cases [2], [6], [9], [18], [20], [34], [37]- [40]. In contrast, in [29], [42], [43], a different 𝐾 is used for target and non-target cases.…”

Section: A Strategies For Trainingmentioning

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

View full text Add to dashboard Cite

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"...

show abstract

Section: A Strategies For Trainingmentioning

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

View full text Add to dashboard Cite

show abstract

“…The GPR measures variations in electromagnetic properties of materials, through the use of propagating Electromagnetic Waves. (Chomdee & Boonpoonga, 2014). The basic property of propagation that provides GPR with a special opportunity for producing enhanced images of materials in the subsurface is the polarization of electromagnetic waves.…”

Section: Introductionmentioning

confidence: 99%

Measurement of Depth Resolutions of GPR Antenna

Adams,

Davordzie

2023

Preprint

View full text Add to dashboard Cite

This research work was aimed at investigating and determining the depth resolution of a 500Mhz GPR Antenna. The survey was conducted in an open space at the Physics Department, College of science-KNUST. The MALA GPR equipment with 500MHz shielded Antenna in the common offset-mode was used to investigate the depth at which materials were buried. Concrete block, tree roots, plastic bottle with water, glass bottle and Electric wire were each buried in cavities with five different burial depths. The real depth of each material was measured both along and across the cavity and compared to their corresponding GPR measured depth. The comparison of the Real depth and the GPR depth showed almost identical depth measurements with a root mean square deviation of 0.028m. The implication of this is that the 500 Mhz GPR antenna can accurately locate and detect the position of buried materials in the subsurface at a depth within the range (0.20-0.66) m.

show abstract

“…In (Pasolli et al, ), the signal processing for detecting objects and recognizing its material type was subdivided into four main stages, that is, preprocessing, image segmentation, object detection, and material recognition stages. Recently, a technique for real‐time detection was presented (Chomdee et al, ). This approach can detect buried objects based on a B‐scan image, employing a simple equation instead of using advanced techniques, in order to reduce the processing time.…”

Section: Introductionmentioning

confidence: 99%

Automatic Detection and Classification of Buried Objects Using Ground‐Penetrating Radar for Counter‐Improvised Explosive Devices

et al. 2018

Self Cite

View full text Add to dashboard Cite

In this paper, a technique that can automatically detect and classify objects buried under the ground is proposed. The technique employs a ground‐penetrating radar that transmits electromagnetic waves in order to strike the objects and then receives the backscattering electromagnetic wave to perform signal processing. This signal processing is divided into four main steps as follows. First, preprocessing is used to reduce the clutter due to the effect of the media layer interface. Second, the late time of the scattering signal is estimated using a simple cross correlation. Third, a few successive poles are extracted from the scattering response at the estimated late time by using the short‐time matrix pencil method. Finally, the extracted poles are fed for object classification with different constitutions and/or shapes using a support vector machine. Simulations according to the practical situation in three southern provinces of Thailand to counter the improvised explosive devices were set up. The performance of the proposed technique was evaluated. The simulation results showed that the proposed technique can efficiently detect and classify buried objects for counter‐improvised explosive device operations in the military.

show abstract

Fast and efficient detection of buried object for GPR image

Cited by 10 publications

References 4 publications

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

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

Measurement of Depth Resolutions of GPR Antenna

Automatic Detection and Classification of Buried Objects Using Ground‐Penetrating Radar for Counter‐Improvised Explosive Devices

Contact Info

Product

Resources

About