Improving seabed classification from Multi-Beam Echo Sounder (MBES) backscatter data with visual data mining

Abstract

Multi-Beam Echo Sounders are often used for classification of seabed type, as there exists a strong link between sonar backscatter and sediment characteristics of the seabed. Most of the methods for seabed classification from MBES backscatter create a highly-dimensional data set of statistical features and then use a combination of Principal Component Analysis and k-means clustering to derive classes. This procedure can be time consuming for contemporary large MBES data sets with millions of records. This paper examines the complexity of one of most commonly used classification approaches and suggests an alternative where feature data set is optimised in terms of dimensionality using computational and visual data mining. Both the original and the optimised method are tested on an MBES backscatter data set and validated against ground truth. The study found that the optimised method improves accuracy of classification and reduced complexity of processing. This is an encouraging result, which shows that bringing together methods from acoustic classification, visual data mining, spatial analysis and remote sensing can support the unprecedented increases in data volumes collected by contemporary acoustic sensors.