Segmentation of multibeam acoustic imagery in the exploration of the deep sea-bottom

The new generation of low-frequency echosounders, primarily used for bathymetric purposes, are also able to record acoustic images of the sea floor. Reflected energy, as a function of the incidence angle, is known to be strongly dependent on seabed type, and therefore stands as a potential tool in sea floor characterization. On the other hand, acoustic images of the reflected energy (mosaics), illustrate the variability of the acoustic interface and are invaluable for sea floor cartography. We describe a method of semi-automatic mosaic interpretation where the two different aspects are considered simultaneously. This is achieved by supervised segmentation using a Markov Random Field model where the neighbourhood system and energies have been carefully studied in order to comply to a priori knowledge. We present results obtained with this method, enhancing the possibility of using such a technique for low-frequency echosounders