Combination of Support Vector Machine and H-Alpha Decomposition for Subsurface Target Classification of GPR

Zhou, Haoqiu; Feng, Xuan; Zhang, Yan; Nilot, Enhedelihai; Zhang, Minghe; Dong, Zejun; Qi, Jiahui

doi:10.1109/icgpr.2018.8441522

Cited by 10 publications

(4 citation statements)

References 8 publications

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“…For a quantitative study, we calculate ROC plots (true positive rate versus false positive rate parameterized by the detection threshold) 6 on the outputs of the considered methods.…”

Section: Performance Of Hub-gpr Algorithmmentioning

confidence: 99%

New Robust Sparse Convolutional Coding Inversion Algorithm for Ground Penetrating Radar Images

Gallet

Mian

Ginolhac

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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In this paper, we propose two algorithms to enhance the interpretability of the hyperbola in B-scans obtained with a Ground Penetrating Radar (GPR). These hyperbolas are the responses of buried objects or cavities. To correctly detect and classify them, a denoising is typically necessary for GPR images as the signal-to-noise ratio is low, and the various interfaces naturally present in the earth have a strong response. Both algorithms are based on a sparse convolutional coding model plus a low rank component. It is solved through an Alternating Direction Method of Multipliers (ADMM) framework. In order to take into account the presence of outliers and the artifacts caused by the acquisition, the second algorithm is based on the Huber norm instead of the classic L2-norm. These algorithms are tested on a real dataset labeled by geophysicists. The results show the denoising efficiency of this approach, and in particular the robustness of the second algorithm.

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“…For a quantitative study, we calculate ROC plots (true positive rate versus false positive rate parameterized by the detection threshold) 6 on the outputs of the considered methods.…”

Section: Performance Of Hub-gpr Algorithmmentioning

confidence: 99%

New Robust Sparse Convolutional Coding Inversion Algorithm for Ground Penetrating Radar Images

Gallet

Mian

Ginolhac

et al. 2023

IEEE Trans. Geosci. Remote Sensing

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“…et al [8] combined support vector machines (SVMs) and hidden Markov models (HMMs) for Crevasse detection in ice sheets. Zhou et al [9] combined SVM with H-Alpha Decomposition for subsurface target classification of GPR. The existing processing methods are fallible and unreliable for pavement distress detection using 3D GPR data.…”

Section: Introductionmentioning

confidence: 99%

Pavement Distress Detection Using Three-Dimension Ground Penetrating Radar and Deep Learning

et al. 2022

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Three-dimensional ground penetrating radar (3D GPR) is a non-destructive examination technology for pavement distress detection, for which its horizontal plane images provide a unique perspective for the task. However, a 3D GPR collects thousands of horizontal plane images per kilometer of the investigated pavement. The existing detection methods using GPR images are time-consuming and risky for subjective judgment. To solve the problem, this study used deep learning methods and 3D GPR horizontal plane images to detect pavement structural distress, including cracks, repairs, voids, poor interlayer bonding, and mixture segregation. In this study, two deep learning methods, called CP-YOLOX and SViT, were used to achieve the aim. A dataset for anomalous waveform localization (3688 images) was first created by pre-processing 3D-GPR horizontal plane images. A CP-YOLOX model was then trained to localize anomalous waveforms. Five SViT models with different numbers of encoders were adopted to perform the classification of anomalous waveforms using the localization results from the CP-YOLOX model. The numerical experiment results showed that 3D GPR horizontal plane images have the potential to be an assistant for pavement structural distress detection. The CP-YOLOX model achieved 87.71% precision, 80.64% mAP, and 33.57 sheets/s detection speed in locating anomalous waveforms. The optimal SViT achieved 63.63%, 68.12%, and 75.57% classification accuracies for the 5-category, 4-category, and 3-category datasets, respectively. The proposed models outperformed other deep learning methods on distress detection using 3D GPR horizontal plane images. In the future, more radar images should be collected to improve the accuracy of SViT.

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“…We consider presently the second step of classification. This task has been investigated in previous works where it is performed by using Support Vector Machine (SVM) algorithm in [4,5], dictionary learning techniques in [2] and convolutional neural networks (CNN) in [6]. The SVM based approaches rely on either the use of the polarimetry information or Fourier coefficients estimation as features for the classification.…”

Section: Introductionmentioning

confidence: 99%

Classification of GPR Signals Via Covariance Pooling on CNN Features Within a Riemannian Framework

Gallet

Mian

Ginolhac

et al. 2022

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium

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We consider the problem of classifying Ground Penetrating Radar (GPR) signals by using covariance matrices descriptors computed on convolutional features obtained from Mo-bileNetV2 Convolutional Neural Network (CNN) first layers. This approach allows to leverage the rich data representation obtained from CNNs and the low-dimensionality of secondorder statistics. Then the Riemannian geometry of covariance matrices is leveraged to improve classification rate. The proposed approach allows then to perform automatic classification of buried objects with few labeled data available. We also consider the scenario of an airbone radar and provide results at different elevations.

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Combination of Support Vector Machine and H-Alpha Decomposition for Subsurface Target Classification of GPR

Cited by 10 publications

References 8 publications

New Robust Sparse Convolutional Coding Inversion Algorithm for Ground Penetrating Radar Images

New Robust Sparse Convolutional Coding Inversion Algorithm for Ground Penetrating Radar Images

Pavement Distress Detection Using Three-Dimension Ground Penetrating Radar and Deep Learning

Classification of GPR Signals Via Covariance Pooling on CNN Features Within a Riemannian Framework

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