Author: WU Tianbao XIA Jingbo HUANG Yuyan | Time: 2020-07-10 | Counts: |
doi:10.16186/j.cnki.1673-9787.2020.4.17
Received:2019/11/28
Revised:2020/01/09
Published:2020/07/15
Target recognition method of SAR images based on cascade decision fusion ofSVM and SRC
WU Tianbao1, XIA Jingbo1, HUANG Yuyan2
1.School of Information Science and Technology, Xiamen University Tan Kah Kee College, Xiamen 361005,Fujian,China;2.Marine Engineering Institute,Jimei University,Xiamen 361021,Fujian,China
Abstract:A synthetic aperture radar( SAR )target recognition method was proposed based on cascade decision fusion of support vector machine ( SVM )and sparse representation-based classification( SRC ). Firstly,SVM was employed to classify the test sample. Based on the posterior probabilities corresponding to different training classes,a threshold judgement algorithm was used to select those classes with high reliabilities. Secondly,the selected training classes were used to establish a dictionary to further classify the test sample by SRC. Finally,a linear fusion strategy was adopted to combine the results from SVM and SRC to obtain reliable recognition results. The prescreening by SVM could effectively decrease the scale of the dictionary for SRC thus improving the classification efficiency. Meanwhile,SRC had good robustness to conditions like noise corruption and occlusion ,which could complement the shortages of SVM. Therefore, the proposed method could effectively combine the advantages of SVM and SRC to enhance the final recognition performance. The MSTAR dataset was used to conduct target recognition experiments and the results showed the effectiveness of the proposed method.
Key words:synthetic aperture radar;target recognition;cascade decision fusion;support vector machine;sparse representation-based classification
基于SVM和SRC级联决策融合的SAR图像目标识别方法_吴天宝.pdf