Application of Denoising CNN for Noise Suppression and Weak Signal Extraction of Lunar Penetrating Radar Data

Zhou, Haoqiu; Feng, Xuan; Dong, Zejun; Liu, Cai; Liang, Wei

doi:10.3390/rs13040779

Cited by 18 publications

(6 citation statements)

References 51 publications

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“…On the other hand, the output layer works as the decoder to reconstruct the input by the inverse mapping from the latent-space representation. Autoencoders have been widely used in data denoizing, dimensionality reduction, and image generation [38][39][40][41][42]. In [26], an autoencoder-based clutter mitigation method was proposed due to the advantage that this technique requires neither prior information regarding the penetrable medium characteristics nor analytic framework to describe the through-medium interference.…”

Section: Theory Of Autoencodermentioning

confidence: 99%

Learning-Based Clutter Mitigation with Subspace Projection and Sparse Representation in Holographic Subsurface Radar Imaging

Chen¹,

Liu²,

Liu³

et al. 2022

Remote Sensing

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The holographic subsurface radar (HSR) is an effective remote sensing modality for surveying shallowly buried objects with high resolution images in plan-view. However, strong reflections from the rough surface and inhomogeneities obscure the detection of stationary targets response. In this paper, a learning-based method is proposed to mitigate the clutter in HSR applications. The proposed method first decomposes the HSR image into raw clutter and target data using an adaptive subspace projection approach. Then, the autoencoder is applied to carry out unsupervised learning to extract the target features and mitigate the clutter. The sparse representation is also combined to further optimize the model and the alternating direction multiplier method (ADMM) is used to solve the optimization problem for precision and efficiency. Experiments using real data were conducted to demonstrate that the proposed method can effectively mitigate the strong clutter with the target preserved. The visual and quantitative results show that the proposed method achieves superior performance on suppressing clutter in HSR images compared with the widely used state-of-the-art clutter mitigation approaches.

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Section: Theory Of Autoencodermentioning

confidence: 99%

Learning-Based Clutter Mitigation with Subspace Projection and Sparse Representation in Holographic Subsurface Radar Imaging

Chen¹,

Liu²,

Liu³

et al. 2022

Remote Sensing

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“…Ahmed et al, 2010 ; Fawaz et al, 2019 ; Pavlyshenko, 2019 ; Lim and Zohren, 2021 and references therein), as well as image denoising (e.g., Refs. Pravin and Ojha, 2020 ; Ilesanmi and Ilesanmi, 2021 ; Zhou et al, 2021 and references therein).…”

Section: Introductionmentioning

confidence: 99%

Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks

Adhikari

2022

Front. Artif. Intell.

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Currently, the sub-60 Hz sensitivity of gravitational-wave (GW) detectors like Advanced LIGO (aLIGO) is limited by the control noises from auxiliary degrees of freedom which nonlinearly couple to the main GW readout. One promising way to tackle this challenge is to perform nonlinear noise mitigation using convolutional neural networks (CNNs), which we examine in detail in this study. In many cases, the noise coupling is bilinear and can be viewed as a few fast channels' outputs modulated by some slow channels. We show that we can utilize this knowledge of the physical system and adopt an explicit “slow×fast” structure in the design of the CNN to enhance its performance of noise subtraction. We then examine the requirements in the signal-to-noise ratio (SNR) in both the target channel (i.e., the main GW readout) and in the auxiliary sensors in order to reduce the noise by at least a factor of a few. In the case of limited SNR in the target channel, we further demonstrate that the CNN can still reach a good performance if we use curriculum learning techniques, which in reality can be achieved by combining data from quiet times and those from periods with active noise injections.

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“…Zhou et al . (2021) introduced the convolutional neural network to GPR denoising and improved the quality of denoising results.…”

Section: Introductionmentioning

confidence: 99%

“…Bi et al (2018) improved singular value decomposition (SVD) by combining it with the Hankel matrix to remove clutter and random noise while weakening some multiple waves. Zhou et al (2021) introduced the convolutional neural network to GPR denoising and improved the quality of denoising results.…”

mentioning

confidence: 99%

GPR denoising via shearlet transformation and a data‐driven tight frame

Zhang

Tang

et al. 2022

Near Surface Geophysics

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Ground‐penetrating radar (GPR) is commonly used to detect buried and near‐surface geophysical structures. GPR denoising is necessary because some level of interference, such as from clutter, random noise and/or the column artefact, are inevitable and can cause false geological interpretations. Existing sparse representation methods, including wavelet transformation, curvelet transformation and dictionary learning, are critical in GPR denoising. However, they perform poorly in some cases because GPR data cannot be represented efficiently under severe interference. Thus, this study proposes an approach that combines shearlet transformation (ST) and a data‐driven tight frame (DDTF) to improve data sparsity. The ST can provide the prior information of GPR data to the DDTF, while the DDTF can self‐adaptively represent GPR data. First, we separate significant reflections and interferences using ST. Second, we apply the DDTF to further suppress the interferences by setting different thresholds in different ST scales and directions. Finally, we adopt inverse transformations to recover the GPR data. In the experiments, ST is used to show the differences between the significant reflections and interferences of the synthetic GPR data. We also sequentially remove each interference of the synthetic GPR data to clearly highlight the performance of the method. To ensure the effectiveness of the ST‐DDTF approach, we test the method using synthetic GPR data from different models, along with some example field GPR data. The ST‐DDTF method, which is aimed at improving data sparsity, shows state‐of‐the‐art results relative to more standard GPR denoising methods. Although our approach is time consuming, it is useful in processing small GPR data and obtaining accurate denoising results.

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Application of Denoising CNN for Noise Suppression and Weak Signal Extraction of Lunar Penetrating Radar Data

Cited by 18 publications

References 51 publications

Learning-Based Clutter Mitigation with Subspace Projection and Sparse Representation in Holographic Subsurface Radar Imaging

Learning-Based Clutter Mitigation with Subspace Projection and Sparse Representation in Holographic Subsurface Radar Imaging

Nonlinear Noise Cleaning in Gravitational-Wave Detectors With Convolutional Neural Networks

GPR denoising via shearlet transformation and a data‐driven tight frame

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