Wavelet analysis of bathymetric sidescan sonar data for the classification of seafloor sediments in Hopvågen Bay - Norway

Abstract

In this paper we examine the use of bathymetric sidescan sonar for automatic classification of seabed sediments. Bathymetric sidescan sonar, here implemented through a small receiver array, retains the advantage of sidescan in speed through illuminating large swaths, but also enables the data gathered to be located spatially. The spatial location allows the image intensity to be corrected for depth and insonification angle, thus improving the use of the sonar for identifying changes in seafloor sediment. In this paper we investigate automatic tools for seabed recognition, using wavelets to analyse the image of Hopvagen Bay in Norway. We use the back-propagation elimination algorithm to determine the most significant wavelet features for discrimination. We show that the features selected present good agreement with the grab sample results in the survey under study and can be used in a classifier to discriminate between different seabed sediments.