scholarly journals GPR Clutter Reflection Noise-Filtering through Singular Value Decomposition in the Bidimensional Spectral Domain

2021 ◽  
Vol 13 (10) ◽  
pp. 2005
Author(s):  
Rui Jorge Oliveira ◽  
Bento Caldeira ◽  
Teresa Teixidó ◽  
José Fernando Borges
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Spectral Domain ◽  
Roman Villa ◽  
Useful Signal ◽  
3D Gpr ◽  
Effective Signal ◽  
Value Decomposition ◽  
Ground Penetrating

Usually, in ground-penetrating radar (GPR) datasets, the user defines the limits between the useful signal and the noise through standard filtering to isolate the effective signal as much as possible. However, there are true reflections that mask the coherent reflectors that can be considered noise. In archaeological sites these clutter reflections are caused by scattering with origin in subsurface elements (e.g., isolated masonry, ceramic objects, and archaeological collapses). Its elimination is difficult because the wavelet parameters similar to coherent reflections and there is a risk of creating artefacts. In this study, a procedure to filter the clutter reflection noise (CRN) from GPR datasets is presented. The CRN filter is a singular value decomposition-based method (SVD), applied in the 2D spectral domain. This CRN filtering was tested in a dataset obtained from a controlled laboratory environment, to establish a mathematical control of this algorithm. Additionally, it has been applied in a 3D-GPR dataset acquired in the Roman villa of Horta da Torre (Fronteira, Portugal), which is an uncontrolled environment. The results show an increase in the quality of archaeological GPR planimetry that was verified via archaeological excavation.

scholarly journals GPR Clutter Reflection Noise Filtering Through Singular Value Decomposition in the Bidimensional Spectral Domain

2021 ◽  
Author(s):  
Rui Jorge Oliveira ◽  
Bento Caldeira ◽  
Teresa Teixidó ◽  
José Fernando Borges
Keyword(s):  
Singular Value Decomposition ◽  
Singular Value ◽  
Spectral Domain ◽  
Roman Villa ◽  
Useful Signal ◽  
3D Gpr ◽  
Effective Signal ◽  
Value Decomposition ◽  
Ground Penetrating

Usually, in ground-penetrating radar (GPR) datasets the user defines the limits between the useful signal and the noise through standard filtering to isolate the effective signal as much as possible. However, there are true reflections that mask the coherent reflectors that can be considered noise. In archaeological sites these clutter reflections are caused by scattering with origin in subsurface elements (e.g., isolated masonry, ceramic objects and archaeological collapses). Its elimination is difficult because the wavelet parameters similar to coherent reflections and there is a risk of creating artifacts. In this study a procedure to filtering the clutter reflection noise (CRN) from GPR datasets is presented. The CRN filter is a singular value decomposition-based method (SVD), applied in the 2D spectral domain. This CRN filtering was tested in a dataset obtained from a controlled laboratory environment, to establish a mathematical control of this algorithm. Also, it has been applied in a 3D-GPR dataset acquired in the Roman villa of Horta da Torre (Fronteira, Portugal), which is an uncontrolled environment. The results show an increase in the quality of archaeological-GPR planimetry that were verified via archaeological excavation.

Enhancement of 3D GPR datasets using singular value decomposition applied in 2D the spectral domain for clutter noise removal

2021 ◽  
Author(s):  
Rui Jorge Oliveira ◽  
Bento Caldeira ◽  
Teresa Teixidó ◽  
José Fernando Borges
Keyword(s):  
Singular Value Decomposition ◽  
Filter Design ◽  
Archaeological Site ◽  
Noise Removal ◽  
Computational Effort ◽  
Singular Value ◽  
Spectral Domain ◽  
Buried Structures ◽  
3D Gpr ◽  
Value Decomposition

<p>The ground-penetrating radar (GPR) datasets obtained in archaeological environments have substantial problems related the presence of clutter noise. These noisy reflections are generated by the heterogeneities of the ground and by the collapses of structures buried in the ground, that can prevent a good assessment of the subsurface with this method. The classic filtering operations available can fail to remove it effectively. This work presents an approach to filtering the GPR data in the 2D spectral domain through the singular value decomposition (SVD) factorization technique. The spectral domain present advantages such as the circular symmetry of the transformed data that turns easy the filter parametrisation and the constant computational effort whatever the amount of data considered. SVD allows the decreasing of the user dependency to parametrize the filter. The main propose of this method is to classify automatically the datasets into useful information, corresponding to buried structures, and noise, to remove the last. This approach was conceived based on the study of the GPR signal in the 2D spectral domain and the manual filter design. The tests were performed with different datasets, one from a laboratory experiment (controlled environment) and the other from a field acquisition in an archaeological site (uncontrolled environment) with subsequent excavation to proof the results. The proposed approach is effective to remove the clutter noise in the GPR datasets and constitutes a complementary operation to those already existing in the commercial software.</p><p> </p><p>Acknowledgment: The work was supported by the Portuguese Foundation for Science and Technology (FCT) project UIDB/04683/2020 - ICT (Institute of Earth Sciences) and by the INTERREG 2014-2020 Program, through the "Innovación abierta e inteligente en la EUROACE" Project, with the reference 0049_INNOACE_4_E.</p>

scholarly journals Observation Strategies Based on Singular Value Decomposition for Ocean Analysis and Forecast

Water ◽  
2020 ◽  
Vol 12 (12) ◽  
pp. 3445
Author(s):  
Maria Fattorini ◽  
Carlo Brandini
Keyword(s):  
Singular Value Decomposition ◽  
Maximum Error ◽  
Ocean Model ◽  
Singular Value ◽  
Practical Applications ◽  
Positioning Strategy ◽  
Value Decomposition ◽  
Observation Strategies

In this article, we discuss possible observing strategies for a simplified ocean model (Double Gyre (DG)), used as a preliminary tool to understand the observation needs for real analysis and forecasting systems. Observations are indeed fundamental to improve the quality of forecasts when data assimilation techniques are employed to obtain reliable analysis results. In addition, observation networks, particularly in situ observations, are expensive and require careful positioning of instruments. A possible strategy to locate observations is based on Singular Value Decomposition (SVD). SVD has many advantages when a variational assimilation method such as the 4D-Var is available, with its computation being dependent on the tangent linear and adjoint models. SVD is adopted as a method to identify areas where maximum error growth occurs and assimilating observations can give particular advantages. However, an SVD-based observation positioning strategy may not be optimal; thus, we introduce other criteria based on the correlation between points, as the information observed on neighboring locations can be redundant. These criteria are easily replicable in practical applications, as they require rather standard studies to obtain prior information.

scholarly journals Singular value automata and approximate minimization

2019 ◽  
Vol 29 (9) ◽  
pp. 1444-1478 ◽  
Author(s):  
Borja Balle ◽  
Prakash Panangaden ◽  
Doina Precup
Keyword(s):  
Singular Value Decomposition ◽  
Finite Rank ◽  
Singular Value ◽  
Linear Operators ◽  
Hankel Matrices ◽  
Weighted Automata ◽  
Approximate Minimization ◽  
Value Decomposition ◽  
Svd Decomposition

AbstractThe present paper uses spectral theory of linear operators to construct approximatelyminimal realizations of weighted languages. Our new contributions are: (i) a new algorithm for the singular value decomposition (SVD) decomposition of finite-rank infinite Hankel matrices based on their representation in terms of weighted automata, (ii) a new canonical form for weighted automata arising from the SVD of its corresponding Hankelmatrix, and (iii) an algorithmto construct approximateminimizations of given weighted automata by truncating the canonical form.We give bounds on the quality of our approximation.

scholarly journals A Novel Blind and Robust Video Watermarking Technique in Fast Motion Frames Based on SVD and MR-SVD

2018 ◽  
Vol 2018 ◽  
pp. 1-17 ◽  
Author(s):  
Imen Nouioua ◽  
Nouredine Amardjia ◽  
Sarra Belilita
Keyword(s):  
Singular Value Decomposition ◽  
Video Quality ◽  
Singular Value ◽  
Video Watermarking ◽  
Digital Video Watermarking ◽  
Video Frames ◽  
Fast Motion ◽  
Good Transparency ◽  
Value Decomposition

In this work, a novel and efficient digital video watermarking technique based on the Singular Value Decomposition performed in the Multiresolution Singular Value Decomposition domain is proposed. While most of the existing watermarking schemes embed the watermark in all the video frames, which is time-consuming and also affects the perceptibly of the video quality, the proposed method chooses only the fast motion frames in each shot to host the watermark. In doing so, the number of frames to be processed is consequently reduced and a better quality of the watermarked video is also ensured since the human visual system cannot notice the variations in fast moving regions. The watermark information is embedded by Quantization Index Modulation which is a blind watermarking algorithm. The experimental results demonstrate that the proposed method can achieve a very good transparency, while being robust against various kinds of attacks such as filtering, noising, compression, and frame collusion. Compared with several methods found in the literature, the proposed method gives a better robustness.

Singular Value Decomposition-Based Generalized Side Lobe Canceller Beamforming Method for Ultrasound Imaging

2018 ◽  
Vol 32 (07) ◽  
pp. 1854017 ◽  
Author(s):  
Han-Wu Luo ◽  
Fang Li ◽  
Guang Sun ◽  
Shi-Gang Cui ◽  
Nan Lin
Keyword(s):  
Singular Value Decomposition ◽  
Contrast Ratio ◽  
Side Lobe ◽  
Minimum Variance ◽  
Singular Value ◽  
Signal Space ◽  
Velocity Error ◽  
Sample Covariance ◽  
Value Decomposition

In the previous studies, eigenspace-based minimum variance (ESBMV) algorithms were proposed, however, the quality of the algorithm will degrade in low signal to noise occasions. In this study, a singular value decomposition generalized side lobe canceller (SVD-GSC) beamforming method based on the GSC is proposed. The sample covariance matrix is eigendecomposed, and a kind of further SVD is introduced to establish the noise space and the signal space, respectively. After that, the weighting vectors acquired by GSC are projected into the left singular space of the desired signal space. The performance of the proposed method is investigated by both of the simulation and experimental data. And the sound velocity error is also investigated in this paper. The imaging quality of point targets are measured by the [Formula: see text][Formula: see text]dB main lobe width and the peak side lobe (PSL). The contrast ratio (CR) is introduced to describe the quality of cyst phantom. Both the point targets and cyst phantom simulation show that the proposed SVD-GSC performs better in terms of spatial resolution, PSL and CR. Furthermore, the proposed method has a stronger robustness than the traditional GSC.

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