基于随机加权的SAR图像多特征联合目标分类
|
摘要
提出随机加权的合成孔径雷达(SAR)图像多特征联合目标分类方法,多特征决策融合是提高SAR目标分类性能的重要手段之一,然而,不同决策融合机制得到的结果往往大相径庭。首先采用联合稀疏表示对SAR图像中提取的多类特征进行表示,这一步骤主要是在单独表示各类特征的同时挖掘它们之间的关联性;对于各个特征输出的重构误差,并不是采用传统的简单相加而是利用多组随机权值矢量对所有的重构误差进行分析;最后,基于加权后的结果定义决策变量,完成目标类别的判断。在MSTAR数据集上对提出方法进行验证,结果表明其有效性。
This study proposes a synthetic aperture radar( SAR) target classification method based on joint representation of multiple features with random weights. Multi-feature decision fusion is one of the important ways to improve SAR target classification performance.However,the performance of different decision strategies may vary greatly. This paper adopts joint sparse representation to represent the multiple features at first,which aims to exploit the inner correlations of different features. For the reconstruction errors of different features,several random weight vectors are used to analyze them rather than the simple addition in traditional ways. Finally,a decision variable is defined based on the weighted results to judge the target label of the test sample. Results: Experiments are performed on the MSTAR to evaluate the proposed method. The results show the effectiveness of the proposed method.
This study proposes a synthetic aperture radar( SAR) target classification method based on joint representation of multiple features with random weights. Multi-feature decision fusion is one of the important ways to improve SAR target classification performance.However,the performance of different decision strategies may vary greatly. This paper adopts joint sparse representation to represent the multiple features at first,which aims to exploit the inner correlations of different features. For the reconstruction errors of different features,several random weight vectors are used to analyze them rather than the simple addition in traditional ways. Finally,a decision variable is defined based on the weighted results to judge the target label of the test sample. Results: Experiments are performed on the MSTAR to evaluate the proposed method. The results show the effectiveness of the proposed method.
引文
[1]EL-DARYMLI K,GILL E W,MCGUIRE P,et al.Automatic target recognition in synthetic aperture radar imagery:A state-of-the-art review[J].IEEE Access,2016(4):6014-6058.
[2]AMOON M,REZAI-RAD G A.Automatic target recognition of synthetic aperture radar(SAR)images based on optimal selection of Zernike moment features[J].IET Computer Vision,2014,8(2):77-85.
[3]TOUMI A,KHENCHAF A,HOELTZENER B.Aretrieval system from inverse synthetic aperture radar images:Application to radar target recognition[J].Information Sciences,2012(196):73-96.
[4]高君,高鑫,孙显.基于几何特征的高分辨率SAR图像飞机目标解译方法[J].国外电子测量技术,2015,34(8):21-28.GAO J,GAO X,SUN X.Geometrical features-based method for aircraft target interpretation in high-resolution SAR images[J].Foreign Electronic Measurement Technology,2015,34(8):21-28.
[5]MISHRA A K,MOTAUNG T.Application of linear and nonlinear PCA to SAR ATR[C].Radioelektronika.2015:1-6.
[6]韩萍,王欢.结合KPCA和稀疏表示的SAR目标识别方法研究[J].信号处理,2013,29(13):1696-1701.HAN P,WANG H.Research on the synthetic aperture radar target recognition based on KPCA and sparse representation[J].Journal of Signal Processing,2013,29(13):1696-1701.
[7]CUI Z Y,CAO Z J,YANG J Y,et al.Target recognition in synthetic aperture radar via non-negative matrix factorization[J].IET Radar,Sonar and Navigation,2015,9(9):1376-1385.
[8]李帅,许悦雷,马时平,等.基于小波变换和深层稀疏编码的SAR目标识别[J].电视技术,2014,38(13):31-35.LI SH,XU Y L,MA SH P,et al.SAR target recognition using wavelet transform and deep sparse autoencoders[J].Video Engineering,2014,38(13):31-35.
[9]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.DING B Y,WEN G J,YU L SH,et al.Matching of attributed scattering center and its application to synthetic aperture radar automatic target recognition[J].Journal of Radar,2017,6(2):157-166.
[10]DING B Y,WEN G J,ZHONG J R,et al.A robust similarity measure for attributed scattering center sets with application to SAR ATR[J].Neurocomputing,2017(219):130-143.
[11]LIU H C,LI S T.Decision fusion of sparse representation and support vector machine for SAR image target recognition[J].Neurocomputing 2013(113):97-104.
[12]THIAGARAIANM J,RAMAMURTHY K,KNEE P P,et al.Sparse representations for automatic target classification in SAR images[C].4th Communications,Control and Signal Processing,2010:1-4.
[13]CHEN S Z,WANG H P,XU F,et al.Target classification using the deep convolutional networks for SAR images[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(8):4806-4817.
[14]DING J,CHEN B,LIU H W,et al.Convolutional neural network with data augmentation for SAR target recognition[J].IEEE Geoscience and Remote Sensing Letters,2016,13(3):1-5.
[15]YAN Y.Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition[J].Journal of Electronic Imaging,2018,27(2):023024.
[16]HUAN R H,PAN Y.Target recognition for multi-aspect SAR images with fusion strategies[C].Progress in Electromagnetics Research Symposium,2013,134:267-288.
[17]赵鹏举,甘凯.基于互补特征层次决策融合的SAR目标识别方法[J].电光与控制,2018,5(10):28-32.ZHAO P JGAN K.SAR target recognition based on hierarchical decision fusion of complementary features[J].Electronics Optics&Control,2018,25(10):28-32.
[18]JI S H,DUNSON D,CARIN L.Multitask compressive sensing[J].IEEE Transactions on Signal Processing,2009,57(1):92-106.
[19]DONG G G,KUANG G Y,WANG N,et al.SAR target recognition via joint sparse representation of monogenic signal[J].IEEE Journal of Selected Topics Applied Earth Observation and Remote Sensing,2015,8(7):3316-3328.
[20]ZHANG H C,NASRABADI M N,ZHANG Y N,et al.Multi-view automatic target recognition using joint sparse representation[J].IEEE Transactions on Aerospace and Electronic System,2012,48(3):2481-2497.
[2]AMOON M,REZAI-RAD G A.Automatic target recognition of synthetic aperture radar(SAR)images based on optimal selection of Zernike moment features[J].IET Computer Vision,2014,8(2):77-85.
[3]TOUMI A,KHENCHAF A,HOELTZENER B.Aretrieval system from inverse synthetic aperture radar images:Application to radar target recognition[J].Information Sciences,2012(196):73-96.
[4]高君,高鑫,孙显.基于几何特征的高分辨率SAR图像飞机目标解译方法[J].国外电子测量技术,2015,34(8):21-28.GAO J,GAO X,SUN X.Geometrical features-based method for aircraft target interpretation in high-resolution SAR images[J].Foreign Electronic Measurement Technology,2015,34(8):21-28.
[5]MISHRA A K,MOTAUNG T.Application of linear and nonlinear PCA to SAR ATR[C].Radioelektronika.2015:1-6.
[6]韩萍,王欢.结合KPCA和稀疏表示的SAR目标识别方法研究[J].信号处理,2013,29(13):1696-1701.HAN P,WANG H.Research on the synthetic aperture radar target recognition based on KPCA and sparse representation[J].Journal of Signal Processing,2013,29(13):1696-1701.
[7]CUI Z Y,CAO Z J,YANG J Y,et al.Target recognition in synthetic aperture radar via non-negative matrix factorization[J].IET Radar,Sonar and Navigation,2015,9(9):1376-1385.
[8]李帅,许悦雷,马时平,等.基于小波变换和深层稀疏编码的SAR目标识别[J].电视技术,2014,38(13):31-35.LI SH,XU Y L,MA SH P,et al.SAR target recognition using wavelet transform and deep sparse autoencoders[J].Video Engineering,2014,38(13):31-35.
[9]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.DING B Y,WEN G J,YU L SH,et al.Matching of attributed scattering center and its application to synthetic aperture radar automatic target recognition[J].Journal of Radar,2017,6(2):157-166.
[10]DING B Y,WEN G J,ZHONG J R,et al.A robust similarity measure for attributed scattering center sets with application to SAR ATR[J].Neurocomputing,2017(219):130-143.
[11]LIU H C,LI S T.Decision fusion of sparse representation and support vector machine for SAR image target recognition[J].Neurocomputing 2013(113):97-104.
[12]THIAGARAIANM J,RAMAMURTHY K,KNEE P P,et al.Sparse representations for automatic target classification in SAR images[C].4th Communications,Control and Signal Processing,2010:1-4.
[13]CHEN S Z,WANG H P,XU F,et al.Target classification using the deep convolutional networks for SAR images[J].IEEE Transactions on Geoscience and Remote Sensing,2016,54(8):4806-4817.
[14]DING J,CHEN B,LIU H W,et al.Convolutional neural network with data augmentation for SAR target recognition[J].IEEE Geoscience and Remote Sensing Letters,2016,13(3):1-5.
[15]YAN Y.Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition[J].Journal of Electronic Imaging,2018,27(2):023024.
[16]HUAN R H,PAN Y.Target recognition for multi-aspect SAR images with fusion strategies[C].Progress in Electromagnetics Research Symposium,2013,134:267-288.
[17]赵鹏举,甘凯.基于互补特征层次决策融合的SAR目标识别方法[J].电光与控制,2018,5(10):28-32.ZHAO P JGAN K.SAR target recognition based on hierarchical decision fusion of complementary features[J].Electronics Optics&Control,2018,25(10):28-32.
[18]JI S H,DUNSON D,CARIN L.Multitask compressive sensing[J].IEEE Transactions on Signal Processing,2009,57(1):92-106.
[19]DONG G G,KUANG G Y,WANG N,et al.SAR target recognition via joint sparse representation of monogenic signal[J].IEEE Journal of Selected Topics Applied Earth Observation and Remote Sensing,2015,8(7):3316-3328.
[20]ZHANG H C,NASRABADI M N,ZHANG Y N,et al.Multi-view automatic target recognition using joint sparse representation[J].IEEE Transactions on Aerospace and Electronic System,2012,48(3):2481-2497.