Target recognition in SAR images based on variational mode decomposition

doi:10.6046/gtzyyg.2020.02.05

Abstract
Figures/Tables
References
Related Articles
Metrics

Download: PDF(1752 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

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.

Keywords synthetic aperture Radar target recognition variational mode decomposition joint sparse representation

TP753

Issue Date: 18 June 2020

	Service

	E-mail this article
	E-mail Alert
	RSS
	Articles by authors

	Guangyu ZHOU
	Bangquan LIU
	Dan ZHANG

Cite this article:

Guangyu ZHOU,Bangquan LIU,Dan ZHANG. Target recognition in SAR images based on variational mode decomposition[J]. Remote Sensing for Land & Resources, 2020, 32(2): 33-39.

URL:

https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020.02.05 OR https://www.gtzyyg.com/EN/Y2020/V32/I2/33

Fig.1 Procedure of SAR target recognition based on variational mode decomposition

Fig.2 Optical images of the ten targets

Tab.1 Experimental setup under SOC

Tab.2 Confusion matrix of the proposed method under the SOC

Tab.3 Average recognition rates and average experded time under SOC

Tab.4 Experimental setup under configuration variance

Tab.5 Average recogntion rates with different models

Tab.6 Experimental setup under depression angle variance

Fig.3 Average recognition rates under depression angle variance

Fig.4 Performance of different methods under noise corruption

[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(1):6014-6058.
[2]	文贡坚, 朱国强, 殷红成, 等. 基于三维电磁散射参数化模型的SAR目标识别方法[J]. 雷达学报, 2017,6(2):115-135.
[2]	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.
[7]	谢晴, 张洪. SAR图像多层次正则化增强及在目标识别中的应用[J]. 电子测量与仪器学报, 2018,32(9):157-162.
[7]	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.
[9]	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.
[9]	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.
[11]	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.
[11]	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.
[13]	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.
[13]	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 url: https://linkinghub.elsevier.com/retrieve/pii/S0925231216310074
[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.
[21]	陆振宇, 卢亚敏, 夏志巍, 等. 基于变分模态分解和小波分析的语音信号去噪方法[J]. 现代电子技术, 2018,41(13):47-51.
[21]	Lu Z Y, Lu Y M, Xia Z W, et al. Speech signal denoising based on VMD and wavelet analysis[J]. Modern Electronics Technique, 2018,41(13):47-51.
[22]	任刚, 贾继德, 梅检民, 等. 基于变分模态分级的和去趋势波动分析的柴油机振动信号去噪方法[J]. 内燃机工程, 2019,40(2):76-82.
[22]	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.
[23]	刘嘉敏, 彭玲, 袁佳成, 等. 基于二维变分模态分解和自适应中值滤波的图像去噪方法[J]. 计算机应用研究, 2017,34(10):3149-3152.
[23]	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.

[1]	LI Yuan, WU Lin, QI Wenwen, GUO Zhengwei, LI Ning. A SAR image classification method based on an improved OGMRF-RC model[J]. Remote Sensing for Natural Resources, 2021, 33(4): 98-104.
[2]	JIANG Shan, WANG Chun, SONG Hongli, LIU Yufeng. A study of crop planting type recognition based on SAR and optical remote sensing data[J]. Remote Sensing for Land & Resources, 2020, 32(4): 105-110.
[3]	Nianqin WANG, Dejing QIAO, Xiyou FU. An analysis of the influence of filtering parameter on the performance of Goldstein InSAR interfergram filter[J]. Remote Sensing for Land & Resources, 2019, 31(1): 117-124.
[4]	Hongrui ZHENG, Zhigang XU, Le GAN, Ling CHEN, Jinzhong YANG, Peijun DU. Synthetic aperture Radar remote sensing technology in geological application: A review[J]. Remote Sensing for Land & Resources, 2018, 30(2): 12-20.
[5]	XU Bin. Unsupervised classification of fully polarimetric SAR data based on non-Gauss distribution[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(2): 90-96.
[6]	LIU Bin, GE Daqing, LI Man, ZHANG Ling, WANG Yan, GUO Xiaofang, ZHANG Xiaobo. Ground-based interferometric synthetic aperture radar and its applications[J]. REMOTE SENSING FOR LAND & RESOURCES, 2017, 29(1): 1-6.
[7]	QI Shuai, ZHANG Yonghong, WANG Huiqin. Analysis of fire disturbed forests scattering characteristics using polarimetric SAR image[J]. REMOTE SENSING FOR LAND & RESOURCES, 2016, 28(2): 48-53.
[8]	LIU Huiying, GUO Huadong, ZHANG Lu. Approach to the classification of sea ice in Liaodong Bay using HJ-1C SAR data[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(3): 125-129.
[9]	WANG Xingling, HU Deyong, TANG Hong, SHU Yang. Extraction of landslide information from airborne polarimetric SAR images based on Bayes decision theory[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 121-127.
[10]	HOU Yun-feng, YANG Feng-jun, YANG Xiao-yu, WANG Yu-pu. The Method for Target Recognition from Natural Scene Images Based on Morphological Reconstruction[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(3): 11-15.
[11]	GONG Biao, HUANG Wei-gen, CHEN Peng. Ship Wake Detection in ASAR Image Based on Modified Normalized Hough Transform[J]. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(3): 33-37.
[12]	DU Pei-Jun, SUN Dun-Xin, LIN Hui. THE IMPACT OF WINDOW SIZE UPON SAR IMAGE FILTERING[J]. REMOTE SENSING FOR LAND & RESOURCES, 2006, 18(2): 12-15.
[13]	LIN Hui, DU Pei-jun, SHU Ning, ZHAO Chang-sheng . PHASE NOISE FILTERINGS IN INSAR INTERFEROGRAM[J]. REMOTE SENSING FOR LAND & RESOURCES, 2004, 16(3): 33-36.
[14]	FU Bin, HUANG Wei-gen. SAR SIMULATION STUDY OF STEEP SLOPE BOTTOM TOPOGRAPHY[J]. REMOTE SENSING FOR LAND & RESOURCES, 2003, 15(1): 33-37.
[15]	Rui Benshan, Zhao Yingjun, Pan Wei . APPLICATION OF SAR AND TM IMAGE INTEGRATION TECHNOLOGY IN URANIUM RESOURCE INVESTIGATION IN THE EAST OF ZHUNGEER[J]. REMOTE SENSING FOR LAND & RESOURCES, 1998, 10(3): 61-63.

Viewed

Full text

Abstract

Cited

Shared

Discussed