Target recognition in SAR images based on variational mode decomposition
Guangyu ZHOU1, Bangquan LIU1, Dan ZHANG2
1. College of Digital Technology and Engineering, Ningbo University of Finance and Economics, Ningbo 315175, China 2. College of Computer Science and Technology, Zhejiang University, Hangzhou 310058, China
In order to improve synthetic aperture Radar (SAR) target recognition performance, the authors propose a method based on variational mode decomposition (VMD). First, the bidimensional VMD (BVMD) is employed to decompose SAR images, thus obtaining multi-mode representations. Afterwards, the joint sparse representation is employed to represent the multiple modes. Finally, the target label is determined based on the minimum reconstruction error. The proposed method was tested on the MSTAR dataset. It could achieve a recognition rate of 99.24% on 10 classes of targets under the standard operating condition (SOC). In addition, its performance outperforms some other SAR target recognition methods under configuration variance, depression angle variance, and noise corruption. The results have confirmed the validity of the proposed method.
周光宇, 刘邦权, 张亶. 基于变分模态分解的SAR图像目标识别方法[J]. 国土资源遥感, 2020, 32(2): 33-39.
Guangyu ZHOU, Bangquan LIU, Dan ZHANG. Target recognition in SAR images based on variational mode decomposition. Remote Sensing for Land & Resources, 2020, 32(2): 33-39.
Wen G J, Zhu G Q, Yin H C, et al. SAR ATR based on 3D parametric electromagnetic scattering model[J]. Journal of Radar, 2017,6(2):115-135.
[3]
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.
[4]
Ding B Y, Wen G J, Ma C H, et al. Target recognition in synthetic aperture Radar images using binary morphological operations[J]. Journal of Applied Remote Sensing, 2016,10(4):046006.
[5]
Park J, Park S, Kim Y, et al. New discrimination features for SAR automatic target recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2013,10(3):476-480.
[6]
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.
Xie Q, Zhang H. Multi-level SAR image enhancement based on regularization with application to target recognition[J]. Journal of Electronic Measurement and Instrumentation, 2018,32(9):157-162.
[8]
Mishra, Amit K. Validation of PCA and LDA for SAR ATR [C]//IEEE TENCON.IEEE, 2008: 1-6.
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.
[10]
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.
Li S, Xu Y L, Ma S P, et al. SAR target recognition using wavelet transform and deep sparse autoencoders[J]. Video Engineering, 2014,38(13):31-35.
[12]
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.
Ding B Y, Wen G J, Yu L S, 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.
[14]
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.
doi: 10.1016/j.neucom.2016.09.007
[15]
Tian S R, Yin K Y, Wang C, et al. A SAR ATR method based on scattering centre feature and bipartite graph matching[J]. IETE Technical Review, 2015,32(5):364-375.
[16]
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.
[17]
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.
[18]
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.
[19]
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.
[20]
Dragomiretskiy K, Zosso D. Variational mode decomposition[J]. IEEE Transactions on Signal Processing, 2014,62(3):531-544.
Ren G, Jia J D, Mei J M, et al. Vibration signal denoising method of diesel engines based on VMD and DFA[J]. Chinese International Combustion Engine Engineering, 2019,40(2):76-82.
Liu J M, Peng L, Yuan J C, et al. Image denoising method based on bi-dimensional variational mode decomposition and adaptive median filtering[J]. Application Research of Computers, 2017,34(10):3149-3152.
[24]
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.
[25]
Ji S H, Dunson D, Carin L. Multitask compressive sensing[J]. IEEE Transactions on Signal Processing, 2009,57(1), 92-106.