Fuzzy ARTMAP neural network for seafloor classification from multibeam sonar data

作     者：周兴华 Chen Yongqi Nick Emerson Du Dewen 

作者机构：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 

出 版 物：《High Technology Letters》 (高技术通讯（英文版）)

年 卷 期：2006年第12卷第2期

页      面：219-224页

核心收录：

学科分类：082403[工学-水声工程] 08[工学] 0824[工学-船舶与海洋工程] 

基　　金：国家科技攻关项目(2001AA613040) Hongkong RGC Project(BQ734) 

主　　题：Fuzzy ARTMAP neural network genetic algorithms seafloor classification multibeam sonar 

摘      要：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.