A surface weaving approach to multibeam depth estimation

Arnold, J.; Shaw, Scott

doi:10.1109/oceans.1993.326073

Cited by 5 publications

(7 citation statements)

References 10 publications

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“…In addition, only the original article detailing the CUBE algorithm and its adaptation to a variable resolution scheme [4,17] have been retained in our survey, while a number of explanatory papers and examples of use of the algorithm on various datasets, have not been considered herein. Apart from the CUBE algorithm, a number of other highly ranked citations are essentially based on a data-oriented techniques with the exception of Debese [25,54], Canepa [28] and Shaw's [34]. Being a key element in the development of an outlier detection technique, the representation of the bathymetric information will be discussed first.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…Notably, only [16] uses a dual representation. On the other hand, for surface-oriented methods it can be seen that the majority of the authors uses a spatial representation (i.e., 9 algorithms) of the data or a dual representation (i.e., 5 algorithms) and only two use the sequential representation [34]. This difference between the two main families of techniques can probably be explained by the fact that the surface-oriented methods are based on a seabed modeling, which by its nature is described in geographical space.…”

Section: Summary and Discussionmentioning

confidence: 99%

“…• Isolated outliers (Figure 5b); • Structured groups of outliers (Figure 5a,c,d); • Unstructured groups of outliers (Figure 5d); Some authors mention and propose to detect only one type of error, namely isolated outliers for Shaw [34], spikes for Eeg [35], Ferreira [36] and Bjorke [37] and impulsive noise for Mann [38]. Calder, Du, Li, Lu and Motao [17,27,[39][40][41] oppose this first type of outliers to a second type consisting of groups or clusters of outliers or burst data, emphasizing the difficulty of dealing with them.…”

Section: Deeper Insight Into Outliers In a Hydrographic Contextmentioning

confidence: 99%

“…One of them implements a coarse-to-fine strategy. Shaw [34] uses a global optimization procedure to find a stable surface shape. The bathymetric surface is obtained by minimizing an energy function that combines three competing constraints for surface smoothness, continuity and adherence to the soundings.…”

Section: Surface-oriented Approachesmentioning

confidence: 99%

“…The approaches proposed by Shaw and Motao fall into the first category. The Robust Linear Prediction algorithm implemented by Shaw [34] is an adaptation of an image restoration technique. In its first form, the algorithm successively examines the soundings of a swath.…”

Section: Surface-oriented Approachesmentioning

confidence: 99%

See 4 more Smart Citations

A Review of Data Cleaning Approaches in a Hydrographic Framework with a Focus on Bathymetric Multibeam Echosounder Datasets

et al. 2020

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Automatic cleaning of MultiBeam EchoSounder (MBES) bathymetric datasets is a critical issue in data processing especially with the objective of nautical charting. A number of approaches have already been investigated in order to provide solution in views of operationally reaching this still challenging problem. This paper aims at providing a comprehensive and structured overview of existing contributions in the literature. For this purpose, a taxonomy is proposed to categorize the whole set of automatic and semi-automatic methods addressing MBES data cleaning. The non-supervised algorithms that compose the majority of the methods developed in the hydrographic field, are mainly described according to both the features of the bathymetric data and the type of outliers to detect. Based on this detailed review, past and future developments are discussed in light of both implementation and test on datasets and metrics used for performances assessment.

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Section: Summary and Discussionmentioning

confidence: 99%

Section: Summary and Discussionmentioning

confidence: 99%

Section: Deeper Insight Into Outliers In a Hydrographic Contextmentioning

confidence: 99%

Section: Surface-oriented Approachesmentioning

confidence: 99%

Section: Surface-oriented Approachesmentioning

confidence: 99%

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A Review of Data Cleaning Approaches in a Hydrographic Framework with a Focus on Bathymetric Multibeam Echosounder Datasets

et al. 2020

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From 10 m to 11000 m, Automatic Processing Multi-Beam Bathymetric Data Based on PGO Method

Zhao

Zhou

et al. 2021

IEEE Access

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Multi-beam echo sounders (MBESs) are characterized by the high resolution and high density of the sounding data. The processing of MBES bathymetry data is of special interest currently in marine surveying. The Combined Uncertainty and Bathymetry Estimator (CUBE) and surface filtering are the main MBES-processing algorithms for outliers. These algorithms involve five adjustable parameters; however, few studies have looked at parameter optimization. In this paper, a Parameter Group Optimization (PGO) method that determines the optimal parameters of CUBE and surface filtering based on the seafloor topographic characteristics of the survey area is presented. The method includes typical area selection, optimal grid resolution analysis, parameter group testing and batch processing, sounding and grid analysis. Raw MBES datasets from shallow-and deep-water survey areas (between 10 and 11000 m deep) are used to validate the proposed method. The results show that when the optimized parameters are used in the CUBE and filtering algorithm, the outliers are automatically eliminated; the processed bathymetry data is in good agreement with the bathymetry derived by a traditional manual processing method, while the processing efficiency can be improved by more than 8 times.INDEX TERMS Multi-beam bathymetry, CUBE algorithm, full ocean depth, parameter group optimization.

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Anomaly Detection in Multibeam Bathymetric Point Clouds Integrating Prior Constraints With Geostatistical Prediction

Cui,

Chang,

Zhang

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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A surface weaving approach to multibeam depth estimation

Cited by 5 publications

References 10 publications

A Review of Data Cleaning Approaches in a Hydrographic Framework with a Focus on Bathymetric Multibeam Echosounder Datasets

A Review of Data Cleaning Approaches in a Hydrographic Framework with a Focus on Bathymetric Multibeam Echosounder Datasets

From 10 m to 11000 m, Automatic Processing Multi-Beam Bathymetric Data Based on PGO Method

Anomaly Detection in Multibeam Bathymetric Point Clouds Integrating Prior Constraints With Geostatistical Prediction

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