摘要
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.
出处
《应用科学学报》
EI
CAS
CSCD
北大核心
2010年第4期374-380,共7页
Journal of Applied Sciences
基金
精密工程与工业测量国家测绘局重点实验室开放基金(No.PF2009-21)
国家自然科学基金(No.40704001)资助
关键词
特征提取
全特征向量
主成分分析
海底底质分类
feature extraction
full feature vector
principal component analysis
seabed classification