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 feat...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 3 D space to 2 D 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 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. A classification rate of 84.0% is reached.展开更多
基金supported by the National Natural Science Foundation of China(41676024,41376040,41276039,61271391,61671061)the Post-doctor Foundation of Shaanxi Province(2017BSHQYXMZZ04)the Post-doctor Foundation of the 705th Research Institute,CSIC
文摘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 3 D space to 2 D 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 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. A classification rate of 84.0% is reached.
