Clutter Elimination and Random-Noise Denoising of GPR Signals Using an SVD Method Based on the Hankel Matrix in the Local Frequency Domain

Bi, Wenda; Zhao, Yonghui; An, Chao; Hu, Shufan

doi:10.3390/s18103422

Cited by 32 publications

(14 citation statements)

References 14 publications

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“…Processes that are even more sophisticated have been recently introduced to suppress random noise or the effect of ground clutter in the magnetic data. These include, for example, the application of singular value decomposition (SVD) filtering [104,105], the wavelet transform for signal-noise separation [106,107], spectral analysis, and target resonances [108,109].…”

Section: Removal Of Noisementioning

confidence: 99%

Dreaming of Perfect Data: Characterizing Noise in Archaeo-Geophysical Measurements

Schmidt¹,

Dabas

Sarris

2020

Geosciences

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For the interpretation of archaeological geophysical data as archaeological features, it is essential that the recorded anomalies can be clearly delineated and analyzed, and therefore, care has been taken to obtain the best possible data. However, as with all measurements, data are degraded by unwanted components, or noise. This review clarifies the terminology, discusses the four major sources of noise (instrument, use of instrument, external, soil), and demonstrates how it can be characterized using geostatistical and wavenumber methods. It is important to recognize that even with improved instruments, some noise sources, like soil noise, may persist and that degraded data may be the result of unexpected sources, for example, global positioning system synchronization problems. Suggestions for the evaluation and recording of noise levels are provided to allow estimation of the limit of detection for archaeological geophysical anomalies.

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Section: Removal Of Noisementioning

confidence: 99%

Dreaming of Perfect Data: Characterizing Noise in Archaeo-Geophysical Measurements

Schmidt¹,

Dabas

Sarris

2020

Geosciences

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“…Singular value decomposition (SVD) is a convenient method to decompose a matrix, which can decompose GPR data into different subspaces, and select components containing effective signals to reconstruct GPR signals [24], [25]. The authors in [26] proposed an SVD method based on the Hankel matrix in the local frequency domain for GPR, and handled different numerical models and field GPR data to eliminate the horizontal false signals effectively. On this basis, [27] provided a solution to optimize the size of the Hankel matrix, which can obtain the best noise removal performance for both white noise and correlated noise.…”

Section: Introductionmentioning

confidence: 99%

The Noise Attenuation and Stochastic Clutter Removal of Ground Penetrating Radar Based on the K-SVD Dictionary Learning

et al. 2021

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The ground penetrating radar (GPR) data in the complex detection environment is nonstationary, non-Gaussian, and non-uniform, so the traditional noise attenuation methods are difficult to meet the requirements of denoising. Therefore, we introduced the K-singular value decomposition (K-SVD) dictionary learning into the denoising of GPR signals. It uses the orthogonal matching pursuit (OMP) algorithm to sparse decompose different radar data and trains the overcomplete dictionary with sample characteristics. K-SVD makes full use of the prior information and can extract features according to the sample data adaptively, which means it has strong sparse representation competence. Because radar signals can be sparsely represented in the dictionary, whereas the random noise does not have a sparse representation, the K-SVD dictionary can be used to distinguish effective signals from noise in the GPR data. We used the discrete cosine transform (DCT) and K-SVD dictionaries to process the Gaussian noise and stochastic clutter in the GPR profile. The results show that both K-SVD and DCT can effectively suppress the Gaussian noise. But for the clutter generated by the random medium, the DCT dictionary also causes damage to the effective signals while removing the noise; whereas the K-SVD dictionary learning algorithm uses the DCT dictionary as the initial dictionary and carries out adaptive learning on the noisy data, considering the information in the block and the global observation to complete the denoising, with good denoising effect and high fidelity. We finally verified the effectiveness and practicability of the K-SVD method for measured data.

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“…Singular value decomposition (SVD) is an advantageous filtering technique: reduce high-dimensional, highly variable data to a lower-dimensional space that reveals substructures, discard the unwanted substructures (i.e., direct wave, ringing, random noise), and reconstruct a filtered image. SVD has been previously applied to GPR data e.g., [10][11][12][13][14][15][16][17] with much success. In particular, SVD has been used to increase the resolution of small, point-like reflections in GPR [11].…”

Section: Introductionmentioning

confidence: 99%

Integrated Approach to Investigating Historic Cemeteries

et al. 2020

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Ground-penetrating radar (GPR) and terrestrial laser scanning (TLS) surveys were conducted at a historic cemetery at Cape Canaveral Air Force Station, Florida, U.S., in order to confirm the presence of burials corresponding to grave markers and detect potential unmarked burials. Noise in the GPR data from surface features and subtle terrain differences must be addressed to determine the extent of anomalies of interest. We use singular value decomposition (SVD) to isolate and remove energy from GPR data. SVD allows one to remove unwanted signals that traditional processing techniques cannot. With SVD filtering, we resolve an anomaly adjacent to confirmed burials otherwise overprinted by unwanted signal. The migration of SVD-filtered data produces more distinct, spatially constrained point reflectors. Ground elevation is derived from georeferenced TLS data and compared to that from airborne laser scanning (ALS) to highlight subtle terrain that can assist data interpretation. TLS elevations show a subtle modern mound over the burial plot where ALS elevations show a depression. The targets of interest are approximately 20–30 cm higher in elevation if a topographic correction is performed using TLS versus ALS. In archaeological applications, a notable change is often recorded at the sub-meter scale. The combined approach presented here better resolves geophysical response of buried features and their positions in the ground relative to each other.

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Clutter Elimination and Random-Noise Denoising of GPR Signals Using an SVD Method Based on the Hankel Matrix in the Local Frequency Domain

Cited by 32 publications

References 14 publications

Dreaming of Perfect Data: Characterizing Noise in Archaeo-Geophysical Measurements

Dreaming of Perfect Data: Characterizing Noise in Archaeo-Geophysical Measurements

The Noise Attenuation and Stochastic Clutter Removal of Ground Penetrating Radar Based on the K-SVD Dictionary Learning

Integrated Approach to Investigating Historic Cemeteries

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