SAR ATR Based on FCNN and ICAE

Yu Lingjuan; Wang Yadong; Xie Xiaochun; Lin Yun; Hong Wen

doi:10.12000/JR18066

Volume 7 Issue 5

Nov. 2018

Turn off MathJax

Article Contents

Abstract

References

Article Navigation > Journal of Radars > 2018 > 7(5): 622-631

LIU Pingyu, LYU Xiaode, LIU Zhongsheng, et al. Research on co-channel interference suppression method for passive radar based on the jiont processing of primary and reference channels[J]. Journal of Radars, 2020, 9(6): 974–986. doi: 10.12000/JR19047

Citation:

Yu Lingjuan, Wang Yadong, Xie Xiaochun, Lin Yun, Hong Wen. SAR ATR Based on FCNN and ICAE[J]. Journal of Radars, 2018, 7(5): 622-631. doi: 10.12000/JR18066

Citation:

Yu Lingjuan, Wang Yadong, Xie Xiaochun, Lin Yun, Hong Wen. SAR ATR Based on FCNN and ICAE[J]. Journal of Radars, 2018, 7(5): 622-631. doi: 10.12000/JR18066

PDF( 1055 KB)

SAR ATR Based on FCNN and ICAE

DOI: 10.12000/JR18066

Yu Lingjuan^{1,2
,
,},
Wang Yadong^1
,,
Xie Xiaochun^3
,,
Lin Yun^2
,,
Hong Wen^2
,

①.
School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
②.
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
③.
School of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, China

Funds: The National Natural Science Foundation of China (61431018, 61501210, 61571421), The Natural Science Foundation of Jiangxi Province (20161BAB202054), The Science and Technology Project of Jiangxi Provincial Education Department (GJJ150684, GJJ170825)

Received Date: 2018-08-31
Rev Recd Date: 2018-10-20
Publish Date: 2018-10-28

Abstract

Abstract

In recent years, Synthetic Aperture Radar (SAR) image target recognition based on the Convolutional Neural Network (CNN) has attracted a significant amount of attention. Fully CNN (FCNN) is a structural improvement of the CNN, which features a higher recognition rate than CNN, but it still requires a large number of labeled data in the training process. This study aims to propose a method of SAR image target recognition based on FCNN and Improved Convolutional Auto-Encoder (ICAE), where several parameters of FCNN are initialized by the parameters of the ICAE encoder. These parameters are obtained in the unsupervised training mode. Then, the FCNN is trained by the labeled training samples. The experimental results on 10 kinds of target classification based on the MSTAR datasets show that this method cannot only achieve an average of 98.14% correct recognition rate but also feature a strong anti-noise capability when the labeled training samples are unexpanded.

FullText(HTML)

References(24)

References

[1]	Wagner S A. SAR ATR by a combination of convolutional neural network and support vector machines[J]. IEEE Transactions on Aerospace and Electronic Systems, 2016, 52(6): 2861–2872. DOI: 10.1109/TAES.2016.160061
[2]	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. DOI: 10.1109/TGRS.2016.2551720
[3]	Zhang Z M, Wang H P, Xu F, et al. Complex-valued convolutional neural network and its application in polarimetric SAR image classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(12): 7177–7188. DOI: 10.1109/TGRS.2017.2743222
[4]	Furukawa H. Deep learning for target classification from SAR imagery: Data augmentation and translation invariance[J]. IEICE Technical Report, 2017, 117(182): 13–17.
[5]	徐丰, 王海鹏, 金亚秋. 深度学习在SAR目标识别与地物分类中的应用[J]. 雷达学报, 2017, 6(2): 136–148. DOI: 10.12000/JR16130 Xu Feng, Wang Haipeng, and Jin Yaqiu. Deep learning as applied in SAR target recognition and terrain classification[J]. Journal of Radars, 2017, 6(2): 136–148. DOI: 10.12000/JR16130
[6]	Morgan D A E. Deep convolutional neural networks for ATR from SAR imagery[C]. Proceedings of SPIE 9475, Algorithms for Synthetic Aperture Radar Imagery XXII, Baltimore, Maryland, United States, 2015: 94750F.
[7]	Profeta A, Rodriguez A, and Clouse H S. Convolutional neural networks for synthetic aperture radar classification[C]. Proceedings of SPIE 9843, Algorithms for Synthetic Aperture Radar Imagery XXIII, Baltimore, Maryland, United States, 2016: 98430M.
[8]	Wilmanski M, Kreucher C, and Lauer J. Modern approaches in deep learning for SAR ATR[C]. Proceedings of SPIE 9843, Algorithms for Synthetic Aperture Radar Imagery XXIII, Baltimore, Maryland, United States, 2016: 98430N.
[9]	Wagner S. Combination of convolutional feature extraction and support vector machines for radar ATR[C]. Proceedings of the 17th International Conference on Information Fusion, Salamanca, Spain, 2014: 1–6.
[10]	Pei J F, Huang Y L, Huo W B, et al. SAR automatic target recognition based on multiview deep learning framework[J]. IEEE Transactions on Geoscience and Remote Sensing, 2018, 56(4): 2196–2210. DOI: 10.1109/TGRS.2017.2776357
[11]	Lin Z, Ji K F, Kang M, et al. Deep convolutional highway unit network for SAR target classification with limited labeled training data[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(7): 1091–1095. DOI: 10.1109/LGRS.2017.2698213
[12]	Chen S Z and Wang H P. SAR target recognition based on deep learning[C]. Proceedings of 2014 International Conference on Data Science and Advanced Analytics, Shanghai, China, 2015: 541–547.
[13]	El Housseini A, Toumi A, and Khenchaf A. Deep learning for target recognition from SAR images[C]. Proceedings of 2017 Seminar on Detection Systems Architectures and Technologies, Algiers, Algeria, 2017: 1–5.
[14]	田壮壮, 占荣辉, 胡杰民, 等. 基于卷积神经网络的SAR图像目标识别研究[J]. 雷达学报, 2016, 5(3): 320–325. DOI: 10.12000/JR16037 Tian Zhuangzhuang, Zhan Ronghui, Hu Jiemin, et al. SAR ATR based on convolutional neural network[J]. Journal of Radars, 2016, 5(3): 320–325. DOI: 10.12000/JR16037
[15]	Springenberg J T, Dosovitskiy A, Brox T, et al.. Striving for simplicity: The all convolutional net[OL]. arXiv preprint arXiv:1412.6806, 2015.
[16]	Hinton G E and Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504–507. DOI: 10.1126/science.1127647
[17]	康妙, 计科峰, 冷祥光, 等. 基于栈式自编码器特征融合的SAR图像车辆目标识别[J]. 雷达学报, 2017, 6(2): 167–176. DOI: 10.12000/JR16112 Kang Miao, Ji Kefeng, Leng Xiangguang, et al. SAR target recognition with feature fusion based on stacked autoencoder[J]. Journal of Radars, 2017, 6(2): 167–176. DOI: 10.12000/JR16112
[18]	赵飞翔, 刘永祥, 霍凯. 基于栈式降噪稀疏自动编码器的雷达目标识别方法[J]. 雷达学报, 2017, 6(2): 149–156. DOI: 10.12000/JR16151 Zhao Feixiang, Liu Yongxiang, and Huo Kai. Radar target recognition based on stacked denoising sparse autoencoder[J]. Journal of Radars, 2017, 6(2): 149–156. DOI: 10.12000/JR16151
[19]	Masci J, Meier U, Cireşan D, et al.. Stacked convolutional auto-encoders for hierarchical feature extraction[C]. Proceedings of the 21st International Conference on Artificial Neural Networks on Artificial Neural Networks and Machine Learning – ICANN 2011, Espoo, Finland, 2011: 52–59.
[20]	Vincent P, Larochelle H, Bengio Y, et al.. Extracting and composing robust features with denoising autoencoders[C]. Proceedings of the 25th International Conference on Machine Learning, Helsinki, Finland, 2008: 1096–1103.
[21]	Rumelhart D E, Hinton G E, and Williams R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533–536. DOI: 10.1038/323533a0
[22]	Klambauer G, Unterthiner T, Mayr A, et al.. Self-normalizing neural networks[C]. Proceedings of the 31st Neural Information Processing Systems, Long Beach, CA, United States, 2017: 972–981.
[23]	Kingma D P and Ba J L. Adam: A method for stochastic optimization[OL]. arXiv preprint arXiv:1412.6980, 2015.
[24]	Dong G G, Wang N, and Kuang G Y. Sparse representation of monogenic signal: With application to target recognition in SAR images[J]. IEEE Signal Processing Letters, 2014, 21(8): 952–956. DOI: 10.1109/LSP.2014.2321565