【摘要】 为快速辨别海底底质类型和海底目标,在分析Kohonen自组织特征映射网络(Self-Organizing Feature Map,SOFM)和学习向量量化(Learning Vector Quantization,LVQ)算法的基础上,提出一种SOFM算法与改进的LVQ算法相结合的混合神经网络分类方法.利用这种分类方法,对预处理后的多波束测深系统获取的反向散射强度数据进行训练分类.通过对在实验区域提取的检测样本的分类结果进行比较分析,表明该方法是可行、有效的,而且在底质类型特征相近的情况下,具有较好的分类效果.更多还原

【Abstract】 To identify the types of seabed sediments and target quickly,a hybrid neural network classification method,which combines the Self-Organizing Feature Map( SOFM) algorithm with the modified Learning Vector Quantization( LVQ) algorithm,is proposed by analyzing SOFM and LVQ algorithms developed by Kohonen. The method is used to train and classify the preprocessed seabed backscatter strength data obtained by the multibeam system. By the comparison and analysis on classification results of the test samples in the experimental area,it shows that the method is feasible and effective. In the case of similar seabed sediments,this method is of good classification effect.更多还原