多特征主成分分析与声图相结合的海底底质分类

Seabed Classification Based on Principal Component Analysis of Multiple Features Combined with Sonar Image

通过提取多种回声特征构造全特征向量,并对全特征向量进行主成分分析,计算出对底质分类贡献率最大的特征组,实现海底底质的分类.采用两种分类方法对胶州湾实测数据进行比较,可得出下列结论:应用多特征主成分分析与声图相结合的分类结果优于单纯使用声纳图像的分类结果.

Aimed at seabed classification,statistical characteristics are extracted from the echo,and a full feature vector is constructed.The principal component analysis（PCA） is carried out to obtain the set of characteristics that most contribute to the classification.Based on the study,seabed classification is carried out and tested with two sets of experiments.Using two types of classification methods to analyze,the data from Jiaozhou Bay and comparison is made.It is concluded that the result based on PCA of many features combined with a sonar map is better than that obtained solely from sonar image classification.