Integration of lidar with the NIITEK GPR for improved performance on rough terrain

Ratto, Christopher R.; Morton, Kenneth D.; McMichael, Ian T.; Burns, Brian; Clark, William W.; Collins, Leslie M.; Torrione, Peter A.

doi:10.1117/12.919119

Cited by 6 publications

(4 citation statements)

References 8 publications

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“…This metric is obtained by computing the area under the ROC curve between two false alarm rate (FAR) values (e.g., 0 and 0.005 FAR). pAUC is frequently used for performance comparisons in the BTD literature [33], [37], [55], [57], [58].…”

Section: Cross-validation and Performance Metricsmentioning

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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Section: Cross-validation and Performance Metricsmentioning

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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“…While many of the methods attempt to detect the ground solely using data contained in the GPR returns, additional methods have been proposed that utilize data from secondary "ground imaging" sensors such as light detection and ranging (LIDAR) [3] and electro-optical sensors [4]. This paper presents a robust means of identifying the ground within a GPR image without a secondary sensor using a two-step process.…”

Section: Introductionmentioning

confidence: 99%

Robust entropy-guided image segmentation for ground detection in GPR

Roberts¹,

Shkolnikov²,

Varsanik³

et al. 2013

SPIE Proceedings

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Identifying the ground within a ground penetrating radar (GPR) image is a critical component of automatic and assisted target detection systems. As these systems are deployed to more challenging environments they encounter rougher terrain and less-ideal data, both of which can cause standard ground detection methods to fail. This paper presents a means of improving the robustness of ground detection by adapting a technique from image processing in which images are segmented by local entropy. This segmentation provides the rough location of the air-ground interface, which can then act as a "guide" for more precise but fragile techniques. The effectiveness of this two-step "coarse/fine" entropyguided detection strategy is demonstrated on GPR data from very rough terrain, and its application beyond the realm of GPR data processing is discussed.

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“…The sensing limit of GPR devices change according to the frequency used 18 . The objects are usually buried less than 10 inches deep 7,12,19 . But in this study, there are some objects which are buried 30 cm and 40 cm deep.…”

Section: Dataset Usedmentioning

confidence: 99%

Efficient Underground Object Detection for Ground Penetrating Radar Signals

Mesecan

Bucak

2016

Def. Sc. Jl.

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Ground penetrating radar (GPR) is one of the common sensor system for underground inspection. GPR emits electromagnetic waves which can pass through objects. The reflecting waves are recorded and digitised, and then, the B-scan images are formed. According to the properties of scanning object, GPR creates higher or lower intensity values on the object regions. Thus, these changes in signal represent the properties of scanning object. This paper proposes a 3-step method to detect and discriminate landmines: n-row average-subtraction (NRAS); Min-max normalisation; and image scaling. Proposed method has been tested using 3 common algorithms from the literature. According to the results, it has increased object detection ratio and positive object discrimination (POD) significantly. For artificial neural networks (ANN), POD has increased from 77.4 per cent to 87.7 per cent. And, it has increased from 37.8 per cent to 80.2 per cent, for support vector machines (SVM).

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Integration of lidar with the NIITEK GPR for improved performance on rough terrain

Cited by 6 publications

References 8 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

Robust entropy-guided image segmentation for ground detection in GPR

Efficient Underground Object Detection for Ground Penetrating Radar Signals

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