Range-independent segmentation of sidescan sonar images with unsupervised SOFM Algorithm (Self-Organizing Feature Maps)

The sidescan sonar records the energy of an emitted acoustical wave backscattered by the sea floor, orthogonally to the track followed. The statistical properties of the backscattered energy change with the nature of the sea floor, which allows for a segmentation of the seabed into homogeneous regions. However, the statistical description of the backscattering is not constant over the full swath of the sonar. Making the backscattered energy independent of the grazing angle is a more difficult challenge, conventionally solved by considering a flat seabed and by using physical or an empirical law estimated from the sonar data. To avoid the definition of a physical law describing the change in energy with grazing angle, the proposed algorithm divides the slant range into small stripes, where the statistics can be considered unaltered by the grazing angle variations. The starting stripe at mid sonar slant range is segmented with an unsupervised classifier based on the SOFM (Self-Organizing Feature Maps) algorithm. Then, from the knowledge acquired on the segmentation of this first stripe, the classifier adapts its segmentation to the neighboring stripes. Segmentation performances of the proposed algorithm are compared with those of conventional algorithms.