Automated error detection in multibeam bathymetry data

Shaw, Scott; Arnold, J.

doi:10.1109/oceans.1993.326072

Cited by 15 publications

(5 citation statements)

References 6 publications

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“…Foreign scholars have done a lot of research on the problem of MBES data denoising. In the early days, mathematical statistics were mainly used to detect noise [2][3], such as Ware method [4] and Du method [5]. These methods are not only low in automation, but also the accuracy of the terrain and the accuracy of denoising are not ideal.…”

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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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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“…The combined effect of these residual errors on sounding data will become notable as a range-dependent increase of depths and incident angles of beams leading to a lowquality bathymetric result, shown in Fig. 2 (Canepa et al 2003;Okada et al 2012;Shaw and Arnold 1993). Unfortunately, these residual errors have random characteristics following Gaussian distribution, and so it is hard to estimate them accurately in sounding data processing.…”

Section: Introductionmentioning

confidence: 99%

A new method for weakening the combined effect of residual errors on multibeam bathymetric data

et al. 2014

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Multibeam bathymetric system (MBS) has been widely applied in the marine surveying for providing high-resolution seabed topography. However, some factors degrade the precision of bathymetry, including the sound velocity, the vessel attitude, the misalignment angle of the transducer and so on. Although these factors have been corrected strictly in bathymetric data processing, the final bathymetric result is still affected by their residual errors. In deep water, the result usually cannot meet the requirements of high-precision seabed topography. The combined effect of these residual errors is systematic, and it's difficult to separate and weaken the effect using traditional single-error correction methods. Therefore, the paper puts forward a new method for weakening the effect of residual errors based on the frequency-spectrum characteristics of seabed topography and multibeam bathymetric data. Four steps, namely the separation of the low-frequency and the high-frequency part of bathymetric data, the reconstruction of the trend of actual seabed topography, the merging of the actual trend and the extracted microtopography, and the accuracy evaluation, are involved in the method. Experiment results prove that the proposed method could weaken the combined effect of residual errors on multibeam bathymetric data and efficiently improve the accuracy of the final post-processing results. We suggest that the method should be widely applied to MBS data processing in deep water.

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“…In [6], multiple filtering methods for PFM systems were adopted. Two algorithms for identifying subtle outlier errors in a variety of multibeam systems were developed in [7]. The first algorithm based on robust estimation of autoregressive (AR) model parameters treats the swath as a sequence of images; the other algorithm is based on energy minimization techniques.…”

Section: Introductionmentioning

confidence: 99%

Automatic processing of the multibeam echosounding data based on CUBE algorithm

Zhou

2013

2013 IEEE International Conference on Information and Automation (ICIA)

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Based on combined uncertainty and bathymetry estimator (CUBE) algorithm, a dynamic linear model (DLM) including horizontal and vertical soundings data uncertainties is discussed. Kalman filtering and multiple estimations are utilized to calculate the grid nodes. According to the real measured data of survey area of Bohai, comparison with the manual interactive process, CUBE algorithm is presented to estimate the true depth of the grid points of the survey area in this paper. From the experimental results, it can be seen that the CUBE algorithm is efficient and has good robustness, which means the proposed algorithm is suitable for massive multibeam soundings data in real-time automatic processing. Index Terms -Multibeam echosounding data. CUBE algorithm. Depth estimation. Uncertainty.

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Automated error detection in multibeam bathymetry data

Cited by 15 publications

References 6 publications

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

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

A new method for weakening the combined effect of residual errors on multibeam bathymetric data

Automatic processing of the multibeam echosounding data based on CUBE algorithm

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