Denoising method of ground-penetrating radar signal based on independent component analysis with multifractal spectrum

Li, Ruiheng; Zhang, Hongye; Zheng, Chao; Yu, Nian; Kong, Wenxin; Li, Tianyang; Wang, Enci; Wu, Xialan; Liu, Yang

doi:10.1016/j.measurement.2022.110886

Cited by 61 publications

(24 citation statements)

References 57 publications

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“…In this study, for the purpose of further verification of the proposed surrogate model, new data sets are generated by random noise addition [15,[22][23][24][25][26][27][28] to the generated raw Ascans. The literature offers different approaches to noise incorporation and for different purposes such as data augmentation [14,23,24,29], being closer to realistic scenarios [14,22,24,29,30] and obtaining further verification to test the sensitivity and stability of the considered models [15,[25][26][27][28]. The cases studied in [22,23] are arranged to bring the models closer to the real-time applications, specifically by considering noisy data sets.…”

Section: Noisy Data Sets For Characterization Of Buried Cylindrical P...mentioning

confidence: 99%

“…Therein, random Gaussian noise is inserted to replicate field scenarios [24]. Some subjects on noise suppression and denoising in addition to the mentioned issues follow the mentioned approaches and utilize Gaussian noise addition [25][26][27][28]. In GPR systems, interior or system noise lead to interferences on the reflected signals; it is defined as similar to white Gaussian noise [25].…”

Section: Noisy Data Sets For Characterization Of Buried Cylindrical P...mentioning

confidence: 99%

“…The method has been shown successful for denoising [26] synthetic noisy and practical GPR data. Also, different amplitudes of white Gaussian noise are added to generated signals to test the sensitivity to noise and stability of the model [15,27].…”

Section: Noisy Data Sets For Characterization Of Buried Cylindrical P...mentioning

confidence: 99%

“…As mentioned in Section II.D, for the supplementary evaluation of performance of the proposed model by using noise addition to raw A-scans, new data sets were generated by adding a random noise [15,[22][23][24][25][26][27][28] to the A-scan signals. Thus, the environmental and internal noise of the GPR system and its effect was analyzed.…”

Section: B-scan Evaluationmentioning

confidence: 99%

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Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models

et al. 2023

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This work addresses artificial-intelligence-based buried object characterization using 3-D fullwave electromagnetic simulations of a ground penetrating radar (GPR). The task is to characterize cylindrical shape, perfectly electric conductor (PEC) object buried in various dispersive soil media, and in different positions. The main contributions of this work are (i) development of a fast and accurate data driven surrogate modeling approach for buried objects characterization, (ii) construction of the surrogate model in a computationally efficient manner using small training datasets, (iii) development of a novel deep learning method, time-frequency regression model (TFRM), that employes raw signal (with no pre-processing) to achieve competitive estimation performance. The presented approach is favourably benchmarked against the state-of-the-art regression techniques, including multilayer perceptron (MLP), Gaussian process (GP) regression, support vector regression machine (SVRM), and convolutional neural network (CNN).INDEX TERMS Buried object characterization, ground penetrating radar (GPR), surrogate modeling; microwave modeling; artificial intelligence, A-scan data analysis.

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Section: Noisy Data Sets For Characterization Of Buried Cylindrical P...mentioning

confidence: 99%

Section: Noisy Data Sets For Characterization Of Buried Cylindrical P...mentioning

confidence: 99%

Section: Noisy Data Sets For Characterization Of Buried Cylindrical P...mentioning

confidence: 99%

Section: B-scan Evaluationmentioning

confidence: 99%

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Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models

et al. 2023

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“…Feature extraction and feature selection are two typical dimension-reduction methods. The original hyperspectral datasets are transformed into a low-dimensional and lessredundant feature space by feature extraction and common techniques such as independent component analysis (ICA) [7], principal component analysis (PCA) [8], and local linear embedding (LLE) [9]. Although these methods can extract valuable features from HSI datasets, they often lose physical information of the original data during the process of data compression [10].…”

Section: Introductionmentioning

confidence: 99%

A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm

Cai

Liu

et al. 2022

Symmetry

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Hyperspectral image (HSI) analysis has become one of the most active topics in the field of remote sensing, which could provide powerful assistance for sensing a larger-scale environment. Nevertheless, a large number of high-correlation and redundancy bands in HSI data provide a massive challenge for image recognition and classification. Hybrid Rice Optimization (HRO) is a novel meta-heuristic, and its population is approximately divided into three groups with an equal number of individuals according to self-equilibrium and symmetry, which has been successfully applied in band selection. However, there are some limitations of primary HRO with respect to the local search for better solutions and this may result in overlooking a promising solution. Therefore, a modified HRO (MHRO) based on an opposition-based-learning (OBL) strategy and differential evolution (DE) operators is proposed for band selection in this paper. Firstly, OBL is adopted in the initialization phase of MHRO to increase the diversity of the population. Then, the exploitation ability is enhanced by embedding DE operators into the search process at each iteration. Experimental results verify that the proposed method shows superiority in both the classification accuracy and selected number of bands compared to other algorithms involved in the paper.

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Landslide susceptibility mapping: application of novel hybridization of rotation forests (RF) and Java decision trees (J48)

et al. 2023

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Denoising method of ground-penetrating radar signal based on independent component analysis with multifractal spectrum

Cited by 61 publications

References 57 publications

Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models

Buried Object Characterization Using Ground Penetrating Radar Assisted by Data-Driven Surrogate-Models

A Band Selection Approach for Hyperspectral Image Based on a Modified Hybrid Rice Optimization Algorithm

Landslide susceptibility mapping: application of novel hybridization of rotation forests (RF) and Java decision trees (J48)

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