激光与光电子学进展, 2020, 57 (4): 041004, 网络出版: 2020-02-20   

基于二维经验模态分解的合成孔径雷达目标识别方法 下载: 1070次

Synthetic Aperture Radar Target-Recognition Method Based on Bidimensional Empirical Mode Decomposition
作者单位
1 邵阳学院信息工程学院, 湖南 邵阳 422000
2 湖南第一师范学院信息科学与工程学院, 湖南 长沙 410205
引用该论文

柳小文, 雷军程, 伍雁鹏. 基于二维经验模态分解的合成孔径雷达目标识别方法[J]. 激光与光电子学进展, 2020, 57(4): 041004.

Xiaowen Liu, Juncheng Lei, Yanpeng Wu. Synthetic Aperture Radar Target-Recognition Method Based on Bidimensional Empirical Mode Decomposition[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041004.

参考文献

[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] 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.

    Wen G J, Zhu G Q, Yin H C, et al. SAR ATR based on 3D parametric electromagnetic scattering model[J]. Journal of Radars, 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 moments features[J]. IET Computer Vision, 2014, 8(2): 77-85.

[4] Anagnostopoulos G C. SVM-based target recognition from synthetic aperture radar images using target region outline descriptors[J]. Nonlinear Analysis: Theory, Methods & Applications, 2009, 71(12): e2934-e2939.

[5] Mishra AK. Validation of PCA and LDA for SAR ATR[C]∥IEEE Region 10 Conference, January 1, 2008, Hyderabad,India. China: NSTL, 2008.

[6] Cui Z Y, Cao Z J, Yang J Y, et al. Target recognition in synthetic aperture radar images via non-negative matrix factorisation[J]. IET Radar, Sonar & Navigation, 2015, 9(9): 1376-1385.

[7] 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 S, et al. Matching of attributed scattering center and its application to synthetic aperture radar automatic target recognition[J]. Journal of Radars, 2017, 6(2): 157-166.

[8] 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.

[9] 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.

[10] Thiagarajan JJ, Ramamurthy KN, KneeP, et al. Sparse representations for automatic target classification in SAR images[C]∥2010 4th International Symposium on Communications, Control and Signal Processing (ISCCSP), March 3-5, 2010, Limassol, Cyprus.New York: IEEE, 2010: 11305921.

[11] 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.

[12] 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): 364-368.

[13] 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.

[14] Huang N E, Shen Z, Long S R, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences, 1998, 454(1971): 903-995.

[15] 郑有志, 覃征. 基于二维经验模态分解的医学图像融合算法[J]. 软件学报, 2009, 20(5): 1096-1105.

    Zheng Y Z, Qin Z. Medical image fusion algorithm based on bidimensional empirical mode decomposition[J]. Journal of Software, 2009, 20(5): 1096-1105.

[16] Qin Y, Qiao L H, Wang Q F, et al. Bidimensional empirical mode decomposition method for image processing in sensing system[J]. Computers & Electrical Engineering, 2018, 68: 215-224.

[17] 景娟娟, 相里斌, 李然, 等. 基于经验模态分解的干涉图滤波方法[J]. 光学学报, 2013, 33(10): 1007001.

    Jing J J, Xiangli B, Li R, et al. Interferogram filtering method based on empirical mode decomposition[J]. Acta Optica Sinica, 2013, 33(10): 1007001.

[18] 沈毅, 张敏, 张淼. 基于互信息波段选择和经验模态分解的高精度高光谱数据分类[J]. 激光与光电子学进展, 2011, 48(9): 091001.

    Shen Y, Zhang M, Zhang M. Mutual information bands selection and empirical mode decomposition based support vector machines for hyperspectral data high-accuracy classification[J]. Laser & Optoelectronics Progress, 2011, 48(9): 091001.

[19] 郭心骞, 邱选兵, 季文海, 等. 基于经验模态分解的可调谐半导体激光吸收光谱中干涉条纹的抑制[J]. 激光与光电子学进展, 2018, 55(11): 113001.

    Guo X Q, Qiu X B, Ji W H, et al. Minimization of interference fringes in tunable diode laser absorption spectrum based on empirical mode decomposition[J]. Laser & Optoelectronics Progress, 2018, 55(11): 113001.

[20] Dong G G, Kuang G Y. Classification on themonogenic scale space: application to target recognition in SAR image[J]. IEEE Transactions on Image Processing, 2015, 24(8): 2527-2539.

[21] Chang C C, Lin C J. LIBSVM: a library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 27.

柳小文, 雷军程, 伍雁鹏. 基于二维经验模态分解的合成孔径雷达目标识别方法[J]. 激光与光电子学进展, 2020, 57(4): 041004. Xiaowen Liu, Juncheng Lei, Yanpeng Wu. Synthetic Aperture Radar Target-Recognition Method Based on Bidimensional Empirical Mode Decomposition[J]. Laser & Optoelectronics Progress, 2020, 57(4): 041004.

本文已被 6 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!