Seafloor sediment classification from single beam echo sounder data using LVQ network

Seafloor sediment classification based on echo characteristics obtained from single-beam echosounder is very useful in remote and instant sediment classification. Results of different classification techniques using such data provide robust results when the acoustic beam has a normal incidence with the seabottom. This may not always be true and show poor classification, with the data acquired during rough sea periods corresponding to both oblique and normal incidence of the acoustic pulse, due to roll and pitch motion of the ship. In the present study, an attempt is made to exploit the artificial neural network (ANN) techniques for better classification with such data. Learning Vector Quantisation (LVQ) is a supervised learning algorithm of ANN that is found to be an effective tool and show good performance. The input data to the network include the roughness index (E1) and hardness index (E2) derived from echo characteristics. The network utilizes the competitive learning, a distance function in the first layer and a linear function in the second layer. The network was tried with a different size of hidden neurons and training data size to see the influence on classification. It is found that with ten neurons in the first layer and four neurons in the second layer good performance in classification for the data was achieved.