Multi-Feature Extraction and Fusion for the Underwater Moving Targets Classification

Abstract

The underwater moving targets classfication are important for the underwater surveillance system.This paper presents the classification algrithms based on the multi-feature fusion, including the target echo highlight features,temporal features from the assocition images interframe,and the moving features after tracking.The principal compoment analysis was used to reduce the feature dimension and the k-means algorithm was used for classification. At last,the experiment results of the classification between the divers and underwater vehicles are given, which show that the multifeature fusion can improve the classification performance.And the PCA algorithm can reduce the feature dimension without lower the identification probability.