Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/26899
Title: Seafloor classification of multibeam sonar data using neural network approach
Authors: Zhou, X
Chen, Y 
Keywords: Backscatter strength
Proportional learning vector quantization (PLVQ)
Seafloor classification
Self-organizing map (SOM)
Issue Date: 2005
Source: Marine geodesy, 2005, v. 28, no. 2, p. 201-206 How to cite?
Journal: Marine Geodesy 
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.
URI: http://hdl.handle.net/10397/26899
ISSN: 0149-0419
DOI: 10.1080/01490410590953785
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