Robust Methodology for Detection of Spikes in Multibeam Echo Sounder Data

Abstract

Currently, during the operation in shallow waters, scanning systems, such as multibeam systems, are capable of collecting thousands of points in a short time, promoting a greater coverage of the submerged bottom, with consequent increase in the detection capacity of objects. Although there has been an improvement in the accuracy of the depths collected, traditional processing, that is, manual, is still required. However, mainly due to the increased mass of data collected, manual processing has become extremely time-consuming and subjective, especially in the detection and elimination of spikes. Several algorithms are used to perform this task, but most of them are based on statistical assumptions hardly met and/or verified, such as spatial independence and normality. In this sense, the goal of this study is to present the SODA (Spatial Outlier Detection Algorithm) methodology, a new method for detection of spikes designed to treat bathymetric data collected through swath bathymetry systems. From computational simulation, promising results were obtained. SODA, in some cases, was capable to identify even 90% of spikes inserted on simulation, showing that the methodology is efficient and substantial to the bathymetric data treatment.

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

“…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.…”

“…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.…”

“…In this step, the MAIB (Methodology for the Evaluation of the Uncertainty of Bathymetric data), developed by Ferreira (2018) was used. In all phases that demanded an investigation by outliers, SODA was applied (FERREIRA et al 2019a;FERREIRA et al 2019b).…”

“…This linear overlap value is recommended for the correct processing of the collected data, as well as the visualization of random effects that negatively interfere with the quality of a hydrographic survey. The correct overlap also allows to make judgments about anomalous depths (spikes) (FERREIRA et al, 2019a). It is also possible to use this information to assess the bathymetric survey quality, either by verifying the vertical and horizontal fit of the profiles generated from the successive swaths, or by generating discrepancy samples.…”

“…According to Ferreira et al (2019a), the verification of the vertical and horizontal fit is proven to be effective in assessing the survey quality, being applied in the quality control of the data collected by swaths systems (multibeam echo sounders, bathymetric LiDAR, etc.). However, using these data to estimate sample uncertainties through sample discrepancies is not so common.…”