Spatial outliers detection algorithm (SODA) applied to multibeam bathymetric data processing

Abstract

The amount of data produced in an echosounder has increased dramatically with scanning systems such as multibeam echo sounders and interferometric sonars, providing a considerable improvement in the submerged terrain representation, especially when it comes to detecting hazardous objects to the navigator. However, the available processing algorithms did not come along with this evolution; manual processing or, at least, constant intervention is usually necessary, which makes this task arduous with a high degree of subjectivity. In addition, statistical inconsistencies do not appear to be uncommon in most of the algorithms and filters available. Thus, SODA (Spatial Outliers Detection Algorithm) was recently presented, being a methodology directed, at first, to echosounder data treatment. Considering this, this article aims to evaluate the SODA efficiency for real data treatment from a multibeam sonar. SODA, in some cases, was capable to identify up to 74% of spikes with the δ-Method, which means that this technique detected 28 out of the 38 confirmed spikes in the data set, reassuming the methodology strength regarding the search for spikes in echosounder data.