水下目标亮点拓扑特征提取及自动识别方法

Multiple highlights topology vector feature extraction and automatic recognition method for underwater target

针对水下目标逆合成孔径声呐（Inverse Synthetic Aperture Sonar,ISAS）图像识别问题中观测角度随机多变,目标结构相互遮挡问题,提出一种基于多亮点拓扑矢量特征的ISAS水下目标识别方法。通过分析ISAS成像过程中散射点位置由三维空间向二维成像平面的投影关系,表明了横向定标后的声呐图像中强亮点之间的距离仅由目标散射结构之间的物理距离决定,据此基于强亮点之间的相互距离,构造能稳定描述不同观测角度下目标的拓扑矢量特征。然后通过K-means聚类获取多聚类中心以克服目标结构互相遮挡造成的亮点缺失问题。最终采用最近邻分类器实现目标识别。水池缩比模型实验表明,该方法对于水下目标的识别率达到84.0%。

To address the randomness of target aspect angle and the incompleteness of observed target in inverse synthetic aperture sonar （ISAS） imaging, a method for target recognition is proposed based on topology vector feature （TVF） of multiple highlights. Analysis of the projection relationship from 3D space to 2D imaging plane in ISAS indicates that the distance between two highlights in the cross-range scale calibrated image is determined by the distance between the corresponding physical scattering centers. Then, TVFs of different targets, which remain stable in various possibilities of target aspect angle, can be built. K-means clustering technique is used to effectively alleviate the effect of the point missing due to incompleteness of the observed target. A nearest neighbor classifier is used to realize the target recognition. The ISAS experimental results using underwater scaled models are provided to demonstrate the effectiveness of the proposed method. The classification rate is up to 84.0%.