An improved algorithm of trend surface filtering based on the natural neighboring points range

Zhang, Zhiheng; Peng, Rencan; Huang, Wenqian; Dong, Jian

doi:10.1201/9781315158570-74

Cited by 2 publications

(3 citation statements)

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“…In Area 2 and Area 3, there was a large gap between the experimental results obtained by the TSF method and the true values, and the difference of standard deviation was 0.2495 and 0.2330, respectively. Because the TSF method was plagued by uncertainties in surface fitting function, incomplete filtering and the unreasonable removal of some water depth points [34]. As a result, in Figures 11b and 12b, the method appeared a large degree of excessive filtering and incomplete filtering.…”

Section: Quantitative Analysismentioning

confidence: 99%

A Novel Cone Model Filtering Method for Outlier Rejection of Multibeam Bathymetric Point Cloud: Principles and Applications

Lv,

Wang,

Huang

et al. 2023

Sensors

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The utilization of multibeam sonar systems has significantly facilitated the acquisition of underwater bathymetric data. However, efficiently processing vast amounts of multibeam point cloud data remains a challenge, particularly in terms of rejecting massive outliers. This paper proposes a novel solution by implementing a cone model filtering method for multibeam bathymetric point cloud data filtering. Initially, statistical analysis is employed to remove large-scale outliers from the raw point cloud data in order to enhance its resistance to variance for subsequent processing. Subsequently, virtual grids and voxel down-sampling are introduced to determine the angles and vertices of the model within each grid. Finally, the point cloud data was inverted, and the custom parameters were redefined to facilitate bi-directional data filtering. Experimental results demonstrate that compared to the commonly used filtering method the proposed method in this paper effectively removes outliers while minimizing excessive filtering, with minimal differences in standard deviations from human-computer interactive filtering. Furthermore, it yields a 3.57% improvement in accuracy compared to the Combined Uncertainty and Bathymetry Estimator method. These findings suggest that the newly proposed method is comparatively more effective and stable, exhibiting great potential for mitigating excessive filtering in areas with complex terrain.

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Section: Quantitative Analysismentioning

confidence: 99%

A Novel Cone Model Filtering Method for Outlier Rejection of Multibeam Bathymetric Point Cloud: Principles and Applications

Lv,

Wang,

Huang

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

“…In the actual detection, multiple beams cannot accurately reflect the real terrain due to many subjective and objective factors, so the measured data should be filtered out [1,2]. In the traditional filtering method, the trend surface filtering method becomes an important filtering algorithm because of its relatively simple and easy to calculate.…”

Section: Introductionmentioning

confidence: 99%

“…However, there are uncontrollable factors such as sudden terrain or similar shipwrecks in real seabed terrain, so surfaces fitted by ordinary polynomials do not accurately reflect these features. A new method of local surface fitting is proposed in reference [1]. This method uses the principal curvature of a local surface composed of partial data points to fit the surface based on the construction of the natural neighbor influence domain.…”

Section: Introductionmentioning

confidence: 99%

An Improved Multi-Beam Trend Surface Imaging Method Based on Surface Curvature Compensation

Jiang¹,

Yan²,

Liu³

2019

dtcse

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For multi-beam trend surface imaging, a polynomial is typically created according to the least squares method to fit a surface similar to the actual data. However, in the filtering process, the fitting result will be different from the real situation due to the particularity of some seabed planes. In order to make the surface fit closer to the actual terrain, the curvature correction can be introduced to correct the error. On the basis of comparing the local curvature of the fitted surface with the local curvature of the actual data, a new curvature correction is obtained, and a new trend plane filtering method is proposed by using the new curvature correction. The effectiveness of the new method is verified by MATLAB simulation. The effect is shown in Figure 2. This method can better preserve the local characteristics of the surface.

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An improved algorithm of trend surface filtering based on the natural neighboring points range

Cited by 2 publications

References 0 publications

A Novel Cone Model Filtering Method for Outlier Rejection of Multibeam Bathymetric Point Cloud: Principles and Applications

A Novel Cone Model Filtering Method for Outlier Rejection of Multibeam Bathymetric Point Cloud: Principles and Applications

An Improved Multi-Beam Trend Surface Imaging Method Based on Surface Curvature Compensation

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