Spatial Outliers Detection Algorithm (Soda) Applied to Multibeam Bathymetric Data Processing

Ferreira, Ítalo Oliveira; Santos, Afonso de Paula dos; Oliveira, Júlio César de; Medeiros, Nilcilene das Graças; Emiliano, Paulo César

doi:10.1590/s1982-21702019000400020

Cited by 3 publications

(4 citation statements)

References 11 publications

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“…Each of the three techniques assigns to each sounding a probability of being an outlier. The performances of these techniques are data dependent [36,51].…”

Section: Statistical-based Approachesmentioning

confidence: 99%

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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“…Each of the three techniques assigns to each sounding a probability of being an outlier. The performances of these techniques are data dependent [36,51].…”

Section: Statistical-based Approachesmentioning

confidence: 99%

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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“…More information on the SODA algorithm and one of the methods proposed in it -can be found in Ferreira et al (2019a) and Ferreira et al (2019b). Where C indicates the type of the submerged morphology.…”

Section: Resultsmentioning

confidence: 99%

“…The P2P method, developed and implemented in the R software (R CORE TEAM, 2020), presents itself as a robust, innovative and easy to apply algorithm whose methodological steps are illustrated in Figure 3. To start the method application, the data collected from the bathymetric survey are subjected to preprocessing based on the SODA (Spatial Outliers Detection Algorithm) algorithm, in which the identification and filtering (or cleaning) of the depth data is performed (FERREIRA et al 2019a;FERREIRA et al 2019b). SODA, developed by Ferreira (2018), is a methodology that provide aids to detect spikes in bathymetric point cloud, specifically multbeam echosounder data, interferometric sonars and airborne laser bathymetric systems.…”

Section: Methodsmentioning

confidence: 99%

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Point to Point: An Alternative Method for Extracting “Homologous Points” in Bathymetric Data Collected With a Multibeam System

et al. 2021

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Due to waterway transport efficiency, mainly for commercial trades, the use of sea/river routes has grown recently. So, the importance of producing high quality nautical charts stands out. A nautical chart is the hydrographic survey final product and its accuracy depends on data quality collected, primarily of the vertical quality (depth). In this sense, despite the theoretical and practical difficulty in obtaining homologous points in hydrographic surveys, even when performing check lines, bathymetric data must always be delivered with a statistically proven confidence level. Thus, this study has two main objectives: i) to propose a method, called Point to Point (P2P), for obtaining "homologous points" for hydrographic surveys carried out with multibeam systems, without resorting to mathematical and/or statistical interpolations, called Point to Point (P2P); ii) to quantify the magnitude of the difference between the statistical evaluation using check lines (CL) and by overlapping successive sounding lines (SL), applying the P2P method. The results showed that P2P is easy to application, provide low computation effort, is robust and consistent. Besides that, was possible to applied successive regular lines to get a validation of the hydrographic survey.

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A Comprehensive Deep Learning-Based Outlier Removal Method for Multibeam Bathymetric Point Cloud

Long

Zhang

Zhao

2023

IEEE Trans. Geosci. Remote Sensing

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Spatial Outliers Detection Algorithm (Soda) Applied to Multibeam Bathymetric Data Processing

Cited by 3 publications

References 11 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

Point to Point: An Alternative Method for Extracting “Homologous Points” in Bathymetric Data Collected With a Multibeam System

A Comprehensive Deep Learning-Based Outlier Removal Method for Multibeam Bathymetric Point Cloud

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