决策可靠性分析及在SAR图像目标识别中的应用
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摘要
决策融合是提高合成孔径雷达(Synthetic Aperture Radar,SAR)目标识别性能的重要手段,然而,可靠性较弱的决策往往会导致最终决策融合的效果变差。将可靠性分析引入基于决策融合的SAR目标识别方法中,分别计算各个决策的可靠性系数并选取可靠性的决策参与最终的决策融合。为了验证方法的有效性,分别将提出的可靠性分析应用于多特征决策融合以及多分类器决策融合并基于MSTAR(Moving and Stationary Target Acquisition and Recognition)数据集进行了目标识别实验。在基于主成分分析、线性鉴别分析和非负矩阵分解三种特征进行多特征决策融合的条件下,所提方法和直接进行决策融合的方法的识别率分别为97.47%和96.50%。在基于K近邻、支持向量机和稀疏表示分类器的多分类器决策融合中,所提方法和直接进行决策融合的方法的识别率分别为97.10%和96.28%。实验结果充分证明了所提方法的有效性。
Decision fusion is an effective way to improve synthetic aperture radar( SAR) target recognition performance.However,the decisions with low reliabilities will impair the fused performance to some extent.Therefore,this paper brings reliability analysis into decision fusion for SAR target recognition.The reliability levels of individual decisions are calculated and only those with high reliability levels are used in the final decision fusion.To validate the effectiveness of the proposed method,the proposed strategy is applied in multi-feature decision fusion and multi-classifier decision and target recognition experiments are conducted on Moving and Stationary Target Acquisition and Recognition( MSTAR) dataset.Principle component analysis( PCA),liner discriminant analysis( LDA),and non-negative matrix factorization( NMF) are used for feature extraction in the multi-feature decision fusion.The proposed method and the direct decision fusion achieve the recognition accuracies of 97.47% and 96.50%,respectively.K-nearest neighbor( KNN),support vector machine( SVM),and sparse representation-based classification( SRC) are used as the classifiers in the multi-classifier decision fusion.The proposed method and the direct decision fusion achieve the recognition accuracies of 97.10% and 96.28%,respectively.The experimental results demonstrate the effectiveness of the proposed method.
Decision fusion is an effective way to improve synthetic aperture radar( SAR) target recognition performance.However,the decisions with low reliabilities will impair the fused performance to some extent.Therefore,this paper brings reliability analysis into decision fusion for SAR target recognition.The reliability levels of individual decisions are calculated and only those with high reliability levels are used in the final decision fusion.To validate the effectiveness of the proposed method,the proposed strategy is applied in multi-feature decision fusion and multi-classifier decision and target recognition experiments are conducted on Moving and Stationary Target Acquisition and Recognition( MSTAR) dataset.Principle component analysis( PCA),liner discriminant analysis( LDA),and non-negative matrix factorization( NMF) are used for feature extraction in the multi-feature decision fusion.The proposed method and the direct decision fusion achieve the recognition accuracies of 97.47% and 96.50%,respectively.K-nearest neighbor( KNN),support vector machine( SVM),and sparse representation-based classification( SRC) are used as the classifiers in the multi-classifier decision fusion.The proposed method and the direct decision fusion achieve the recognition accuracies of 97.10% and 96.28%,respectively.The experimental results demonstrate the effectiveness of the proposed method.
引文
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[5]丁柏圆,文贡坚,余连生,等.属性散射中心匹配及其在SAR目标识别中的应用[J].雷达学报,2017,6(2):157-166.
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[8]THIAGARAIANM J J,RAMAMURTHY K N,KNEE P,et al.Sparse representations for automatic target classification in SAR images[C]//Proceedings of 4th International Symposium on Communication,Control and Signal Processing.Limassols:IEEE,2010:1-4.
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[10]LIU H,LI S.Decision fusion of sparse representation and support vector machine for SAR image target recognition[J].Neurocomputing,2013,113(8):97-104.
[11]DING B,WEN G,MA C,et al.Decision fusion based on physically relevant features for SAR ATR[J].IET Radar Sonar and Navigation,2017,11(4):682-690.
[12]许强,李伟,PIERRE L.深度卷积神经网络在SAR目标识别领域的应用综述[J].电讯技术,2018,58(1):106-112.
[13]CHANG C,LIN C.LIBSVM:a library for support vector machine[J].ACM Transactions on Intelligent Systems and Technology,2012,2(3):389-396.
[2]ANAGNOSTOPOULOS G C.SVM-based target recognition from synthetic aperture radar images using target region outline descriptors[J].Nonlinear Analysis,2009,71(2):2934-2939.
[3]MISHRA A K.Validation of PCA and LDA for SAR ATR[C]//Proceedings of IEEE Region 10 Conference.Hyderabad:IEEE,2008:1-6.
[4]龙泓林,皮亦鸣,曹宗杰.基于非负矩阵分解的SAR图像目标识别[J].电子学报,2010,38(6):1425-1431.
[5]丁柏圆,文贡坚,余连生,等.属性散射中心匹配及其在SAR目标识别中的应用[J].雷达学报,2017,6(2):157-166.
[6]DING B,WEN G,ZHONG J,et al.A robust similarity measure for attributed scattering center sets with application to SAR ATR[J].Neurocomputing,2017,219:130-143.
[7]ZHAO Q,PRINCIPLE J C.Support vector machines for SARautomatic target recognition[J].IEEE Transactions on Aerospace and Electronic System,2001,37(2):643-654.
[8]THIAGARAIANM J J,RAMAMURTHY K N,KNEE P,et al.Sparse representations for automatic target classification in SAR images[C]//Proceedings of 4th International Symposium on Communication,Control and Signal Processing.Limassols:IEEE,2010:1-4.
[9]HUAN R,PAN Y.Decision fusion strategies for SAR image target recognition[J].IET Radar Sonar and Navigation,2011,5(7):747-755.
[10]LIU H,LI S.Decision fusion of sparse representation and support vector machine for SAR image target recognition[J].Neurocomputing,2013,113(8):97-104.
[11]DING B,WEN G,MA C,et al.Decision fusion based on physically relevant features for SAR ATR[J].IET Radar Sonar and Navigation,2017,11(4):682-690.
[12]许强,李伟,PIERRE L.深度卷积神经网络在SAR目标识别领域的应用综述[J].电讯技术,2018,58(1):106-112.
[13]CHANG C,LIN C.LIBSVM:a library for support vector machine[J].ACM Transactions on Intelligent Systems and Technology,2012,2(3):389-396.