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.

Keywords:

• Seafloor classification
• backscatter strength
• proportional learning vector quantization (PLVQ)
• self-organizing map (SOM)

Acknowledgments

This work is supported by The Hong Kong Polytechnic University (Project code: G-V931), and the National 863 Program of China (Project code: 2001AA613040).