Robust Automatic Reduction of Multibeam Bathymetric Data Based on M-estimators

Rezvani, Mohammad-Hadi; Sabbagh, Abbas; Ardalan, Alireza A.

doi:10.1080/01490419.2015.1053639

Cited by 16 publications

(26 citation statements)

References 16 publications

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“…All these solutions are time consuming because they use a big data sets. MBES big data processing [19][20][21][22][23][24][25][26][27][28][29][30] has also been investigated. In [19], authors propose algorithm CUBE (combined uncertainty and bathymetry estimator).…”

Section: Introductionmentioning

confidence: 99%

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

et al. 2019

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Autonomous navigation is an important task for unmanned vehicles operating both on the surface and underwater. A sophisticated solution for autonomous non-global navigational satellite system navigation is comparative (terrain reference) navigation. We present a method for fast processing of 3D multibeam sonar data to make depth area comparable with depth areas from bathymetric electronic navigational charts as source maps during comparative navigation. Recording the bottom of a channel, river, or lake with a 3D multibeam sonar data produces a large number of measuring points. A big dataset from 3D multibeam sonar is reduced in steps in almost real time. Usually, the whole data set from the results of a multibeam echo sounder results are processed. In this work, new methodology for processing of 3D multibeam sonar big data is proposed. This new method is based on the stepwise processing of the dataset with 3D models and isoline maps generation. For faster products generation we used the optimum dataset method which has been modified for the purposes of bathymetric data processing. The approach enables detailed examination of the bottom of bodies of water and makes it possible to capture major changes. In addition, the method can detect objects on the bottom, which should be eliminated during the construction of the 3D model. We create and combine partial 3D models based on reduced sets to inspect the bottom of water reservoirs in detail. Analyses were conducted for original and reduced datasets. For both cases, 3D models were generated in variants with and without overlays between them. Tests show, that models generated from reduced dataset are more useful, due to the fact, that there are significant elements of the measured area that become much more visible, and they can be used in comparative navigation. In fragmentary processing of the data, the aspect of present or lack of the overlay between generated models did not relevantly influence the accuracy of its height, however, the time of models generation was shorter for variants without overlay.

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Section: Introductionmentioning

confidence: 99%

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

et al. 2019

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“…Table 1. Filter algorithm execution time statistics Points Algorithm 1 /s Algorithm 2 /s 5.5×10 5 2.843 5.146 1.0×10 6 5.157 11.086 2.2×10 6 11.462 21.208 4.0×10 6 20.817 40.123 6.3×10 6 31.325 63.749 8.2×10 6 41.357 81.287 1.0×10 7 56.114 103.801 1.2×10 7 71.524 125.660…”

Section: Formatting the Textmentioning

confidence: 99%

“…In response to these problems, many experts have proposed a method combining mathematical statistics with spatial location for MBES data denoising. The most representative methods are threshold filtering [6], angle and gradient filtering [7][8][9], and local terrain fitting methods [10]. The research on domestic multi-beam data denoising theory is slightly later than abroad.…”

Section: Introductionmentioning

confidence: 99%

Research on adaptive trend surface filtering algorithm for multi-beam sounding data based on K-D tree

Zhang

2019

E3S Web Conf.

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In the area with complex terrain changes, the traditional trend surface filtering has the problem that it is impossible to reasonably construct the terrain of the expression area. This paper proposes an adaptive trend surface filtering method based on K-D (K-Dimensional Tree) tree. Based on the K-D tree index, the algorithm divides the MBES (the multi-beam echo sounding system) data into several sub-blocks, and then analyzes each sub-block using trend surface filtering algorithm to more accurately reflect the real terrain. The experimental results show that the algorithm execution time in this case is about twice that of the traditional trend surface filtering in the case of millions of data volumes, and the execution efficiency is within a reasonable range. Compared with the traditional trend surface filtering algorithm, the algorithm has a higher fitting degree with the seabed terrain, and the depth difference distribution between the topographic point and the fitting plane is more concentrated. In addition, the proposed algorithm can effectively identify the outlier noise and the near-field noise in the case of ensuring the authenticity of the terrain, so it provides a useful reference for the denoising processing of MBES.

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“…Rezvani et al provided an automatic filtering method with robust M estimation, which calculated the estimated value of each grid point by iterative derivation, and obtained the corresponding residual to discriminate outliers [26]. The model adopted segmented processing to mitigate the adverse effects of outlier observations.…”

Section: State Of the Artmentioning

confidence: 99%

“…a small positive). In the iteration process, weights of abnormal bathymetric points are decreased continuously, and the calculation accuracy of model coefficients is increased by changing the standardized residual error continuously [26]. Consequently, the abnormal bathymetric points can be recognized accurately.…”

Section: Iterative Estimation Of Equivalence Weightmentioning

confidence: 99%

Detection and Elimination of Bathymetric Outliers in Multibeam Echosounder System Based on Robust Multi - quadric Method and Median Parameter Model

Wang¹,

Zhou²,

Wu³

et al. 2018

JESTR

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Multibeam echosounder system is a dynamic measurement under the continuous motion of measurement platform. Contaminated sporadic outliers are inevitably generated during bathymetric data acquisition due to the interference of underwater environmental effects, such as ocean waves, wind, and tides. A filtering model of robust multi-quadric method based on median parameter was proposed in this study to detect and eliminate the outliers in bathymetric datasets. The submarine topography trend surface model was constructed by the multi-quadric function. Known node parameters in the function were obtained by the threshold extraction method of joining angle, chord with dispersion. The robust iterations of residual error initials and the weight-weakening process of outliers were studied by median parameters and IGGIII equivalent weight function. The validity of the proposed filtering model was verified by experimental datasets of offshore waters, which were collected by BV5000 multibeam side scan sonar. Results demonstrate that the known node selection strategy in this study can remove redundancy information in point set and extract regional characteristic points to the maximum extent. The fitting errors at normal bathymetric points are all below 0.06 m, which are decreased compared with the corresponding magnitude of other schemes (0.16 m, 0.12 m). The residual initial value of robust iteration based on median parameters exhibites relatively high estimation accuracy and detection efficiency. The errors of bathymetric points are relatively small (0.019 m), and the detection number of outliers (150) is efficient in the entire detection region. This work can improve the accuracy and efficiency of underwater terrain detection and lay a foundation for later scientific research and marine exploration.

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Robust Automatic Reduction of Multibeam Bathymetric Data Based on M-estimators

Cited by 16 publications

References 16 publications

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

Methodology for Processing of 3D Multibeam Sonar Big Data for Comparative Navigation

Research on adaptive trend surface filtering algorithm for multi-beam sounding data based on K-D tree

Detection and Elimination of Bathymetric Outliers in Multibeam Echosounder System Based on Robust Multi - quadric Method and Median Parameter Model

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