Seafloor Classification of Multibeam Sonar Data Using Neural Network Approach |
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| Authors: | Xinghua Zhou Yongqi Chen |
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| Institution: | 1. Department of Land Surveying and Geo-Informatics , The Hong Kong Polytechnic University , Hunghon, Kowloon, Hong Kong, China;2. The First Institute of Oceanography, State Oceanic Administration , Qingdao, China;3. Department of Land Surveying and Geo-Informatics , The Hong Kong Polytechnic University , Hunghon, Kowloon, Hong Kong, China |
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| Abstract: | 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. |
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| Keywords: | Seafloor classification backscatter strength proportional learning vector quantization (PLVQ) self-organizing map (SOM) |
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