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Fuzzy ARTMAP neural network for seafloor classification from multibeam sonar data

High Technology Letters 2006年第2期12卷 219-224页

作者：周兴华 Chen Yongqi Nick Emerson Du Dewen The First Institute of Oceanography State Oceanic Administration Qingdao P.R. China The Department of Land Surveying and Geo-Informafics Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong SAR of China

This paper presents a seafloor classification method of multibeam sonar data, based on the use of Adaptive Resonance Theory （ART） neural networks. A general ART-based neural network, Fuzzy ARTMAP, has been proposed for seafloor classification of multibeam sonar data. An evolutionary strategy was used to generate new training samples near the cluster boundaries of the neural network, therefore the weights can be revised and refined by supervised learning. The proposed method resolves the training problem for Fuzzy ARTMAP neural networks, which are applied to seafloor classification of multibeam sonar data when there are less than adequate ground-troth samples. The results were synthetically analyzed in comparison with the standard Fuzzy ARTMAP network and a conventional Bayesian classifier. The conclusion can be drawn that Fuzzy ARTMAP neural networks combining with GA algorithms can be alternative powerful tools for seafloor classification of multibeam sonar data.

关键词： Fuzzy ARTMAP neural network genetic algorithms seafloor classification multibeam sonar

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seafloor Sediment classification Based on Multibeam Sonar Data

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Geo-Spatial Information Science 2004年第4期7卷 290-296页

作者： ZHOUXinghua CHENYongqi FirstInstituteofOceanographyofStateOceanicAdministration 6XianxialingRoadQingdao266061China. 不详

The multibeam sonars can provide hydrographic quality depth data as well as hold the potential to provide calibrated measurements of the seafloor acoustic backscattering strength. There has been much interest in utilizing backscatters and images from multibeam sonar for seabed type identification and most results are obtained. This paper has presented a focused review of several main methods and recent developments of seafloor classification utilizing multibeam sonar data or/and images. These are including the power spectral analysis methods, the texture analysis, traditional Bayesian classification theory and the most active neural network approaches.

关键词： seafloor classification multibeam sonar backscattering strength sonarimages

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