Automated acoustic seabed classification of multibeam images of Stanton Banks

Dividing sidescan images into regions that have similar seabeds is often done by expert interpretation. Automated classification systems are becoming more widely used. This paper describes techniques, based on image amplitudes and texture, that lead to useful and practical automated segmentation of multibeam images. Seabed (or riverbed or lakebed) type affects amplitudes and texture, but so do system operating details and survey geometry. Effects of the last two must be compensated to isolate the effects of seabed type. Images from multibeam surveys are accompanied by bathymetric data from which grazing angles of all sonar footprints can be calculated. By compiling tables of amplitude against range and grazing angle, systematic changes in amplitude with these two variables can be removed consistently. Classification, based on a large number of features, is done in image space to avoid artifacts common in mosaics. Unsupervised segmentation requires clustering, in which records are divided into their natural classes. An objective clustering method using simulated annealing assigns points to classes based on their Bayesian distances from cluster centres. Stanton Banks is a rocky area 100 km north of County Donegal, Ireland, that rises about 100 m above the ocean floor at 180 m. Multibeam images and data from an 80-km2 survey were classified into regions of acoustic similarity. Assigning labels of physical properties to these regions requires non-acoustic ground truth, which was obtained from a series of 105 photographs. Photographic geological assignments were found to correlate well with the acoustic classes.