AN SVM classifier with HMM-based kernel for landmine detection using ground penetrating radar

Hamdi, Anis; Missaoui, Oualid; Frigui, Hichem

doi:10.1109/igarss.2010.5650741

Cited by 20 publications

(9 citation statements)

References 6 publications

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“…Examples of feature-based discriminative classifiers that have been used in this application include K-Nearest Neighbor (KNN), 14 Hidden Markov Models, 15 Support Vector Machines, 16 Random Forest 13 and Multiple-Instance Learning. [17][18][19] Most of these techniques rely on constructing a predictive model to characterize data, and as a consequence are prone to over-fitting the training data.…”

Section: Introductionmentioning

confidence: 99%

A label propagation approach for detecting buried objects in handheld GPR data

Reid

Frigui

2016

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI

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Detection of buried landmines and other explosive objects using ground penetrating radar (GPR) has been investigated for almost two decades and several classifiers have been developed. Most of these methods are based on the supervised learning paradigm where labeled target and clutter signatures are needed to train a classifier to discriminate between the two classes. Typically, large and diverse labeled training samples are needed to improve the performance of the classifier by overcoming noise and adding robustness and generalization to unseen examples. Unfortunately, even though unlabeled GPR data may be abundant, labeled data are often available in small quantities as the labeling process is tedious and can be ambiguous for most of the data. In this paper, we propose an algorithm for detecting landmines and buried objects that uses unlabeled data to help labeled data in the classification process. Our algorithm is graph-based and propagates the nodes labels to neighboring nodes according to their proximity in the feature space. For labeled data, we use a set of prototypes that are extracted from a small set of labeled training samples. For unlabeled data, we use a collection of signatures that are extracted from the vicinity of the alarm being tested. This choice is based on the assumption that many spatially close signatures are expected to have similar features and thus, unlabeled samples can create dense regions that link different regions of the labeled samples and propagate their labels to test samples. In other words, unlabeled samples are explored to create a context for each test alarm. To validate the proposed label propagation based classifier, we use it to detect buried explosive objects in GPR data collected by an experimental hand held demonstrator. We show that our approach is robust and computationally efficient to be used for both target discrimination and prescreening.

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Section: Introductionmentioning

confidence: 99%

A label propagation approach for detecting buried objects in handheld GPR data

Reid

Frigui

2016

Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXI

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“…Proposed algorithms have employed a variety of techniques from statistics [17], [18], computer vision [6], [19], [20], and machine learning [7], [21], [22]. The most successful approach to date involves the use of supervised learning techniques [12], [23]- [27].…”

Section: Introductionmentioning

confidence: 99%

“…In order to train supervised classifiers, they must be provided with examples of data from each class (i.e., threat and nonthreat). As mentioned, training examples most often consist of small image patches that are extracted from B-scans at locations in the GPR volume where useful signals (i.e., those corresponding to both threats, or suspicious non-threats) are estimated to exist [1], [4], [6]- [8], [12], [28]- [32]. In this work, we refer to these useful signal locations as "keypoints".…”

Section: Introductionmentioning

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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“…Alarm classification is a subject of research that has been well explored. Examples of classifiers that have been used include K-Nearest Neighbors (KNN) [19] Support Vector Machines (SVM) [24,27], and Hidden Markov Models (HMM) [21,18,28,29,30]. In this thesis, we compare our proposed LP classifier with the provenly successful KNN classifier.…”

Section: Discrimination Algorithmsmentioning

confidence: 99%

“…In this thesis, we compare our proposed LP classifier with the provenly successful KNN classifier. Other classifiers, such as SVM [24,27] [19], and transfer learning [35].…”

Section: Discrimination Algorithmsmentioning

confidence: 99%

Landmine detection using semi-supervised learning.

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AN SVM classifier with HMM-based kernel for landmine detection using ground penetrating radar

Cited by 20 publications

References 6 publications

A label propagation approach for detecting buried objects in handheld GPR data

A label propagation approach for detecting buried objects in handheld GPR data

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

Landmine detection using semi-supervised learning.

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