Seafloor classification of multibeam sonar data using neural network approach

In this study, the self-organizing map (SOM), which is an unsupervised clustering algorithm,
and a supervised proportional learning vector quantization (PLVQ), are employed
to develop a combined method of seafloor classification using multibeam sonar
backscatter data. The PLVQ is a generalized learning vector quantization based on the
proportional learning law (PLL). The proposed method was evaluated in an area where
there are four types of sediments. The results show that the performance of the proposed
method is better than the SOM and a statistical classification method.