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 11 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

“…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

View full text Add to dashboard Cite

show abstract

“…et al used SVM algorithm to identify concrete internal voids and studied the accuracy of four kernel functions [9]. In 2018, a study used the H-Alpha decomposition (a polarimetric decomposition) to extract the feature and trained an SVM classifier [10]. Tbarki et al developed a COSVM classifier and, different from these mentioned methods based on B-scan data, they took some points of A-scan data as a feature vector for detection of a landmine [11].…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

A Comprehensive Review of Conventional and Deep Learning Approaches for Ground-Penetrating Radar Detection of Raw Data

Bai

Chen

et al. 2023

Applied Sciences

View full text Add to dashboard Cite

Ground-penetrating radar (GPR) is a nondestructive testing technology that is widely applied in infrastructure maintenance, archaeological research, military operations, and other geological studies. A crucial step in GPR data processing is the detection and classification of underground structures and buried objects, including reinforcement bars, landmines, pipelines, bedrock, and underground cavities. With the development of machine learning algorithms, traditional methods such as SVM, K-NN, ANN, and HMM, as well as deep learning algorithms, have gradually been incorporated into A-scan, B-scan, and C-scan GPR image processing. This paper provides a summary of the typical machine learning and deep learning algorithms employed in the field of GPR and categorizes them based on the feature extraction method or classifier used. Additionally, this work discusses the sources and forms of data utilized in these studies. Finally, potential future development directions are presented.

show abstract

“…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

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 11 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

A Comprehensive Review of Conventional and Deep Learning Approaches for Ground-Penetrating Radar Detection of Raw Data

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

Contact Info

Product

Resources

About