• Laser & Optoelectronics Progress
  • Vol. 57, Issue 14, 141029 (2020)
Hong Zhang, Xinlan Zuo, and Yao Huang*
Author Affiliations
  • College of Computer and Information Technology, China Three Gorges University, Yichang, Hubei 443002, China
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    DOI: 10.3788/LOP57.141029 Cite this Article Set citation alerts
    Hong Zhang, Xinlan Zuo, Yao Huang. Feature Selection Based on the Correlation of Sparse Coefficient Vectors with Application to SAR Target Recognition[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141029 Copy Citation Text show less
    References

    [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, 4, 6014-6058(2016).

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

    [3] Zhao P J, Gan K. SAR target recognition based on hierarchical decision fusion of complementary features[J]. Electronics Optics & Control, 25, 28-32(2018).

    [4] Xie Q, Zhang H. Multi-level SAR image enhancement based on regularization with application to target recognition[J]. Journal of Electronic Measurement and Instrumentation, 32, 157-162(2018).

    [5] Mishra A K, Motaung T. Application of linear and nonlinear PCA to SAR ATR[C]∥ 25th International Conference Radioelektronika (RADIOELEKTRONIKA) , April 21-22, 2015, Pardubice, Czech Republic., 349-354(2015).

    [6] Han P, Wang H. Research on the synthetic aperture radar target recognition based on KPCA and sparse representation[J]. Journal of Signal Processing, 29, 1696-1701(2013).

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

    [8] Tian L P, Wang J G. Target recognition of SAR images based on sparse representation of wavelet dictionary[J]. Radar Science and Technology, 12, 44-50, 57(2014).

    [9] 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 in Applied Earth Observations and Remote Sensing, 8, 3316-3328(2015).

    [10] Zhang R, Hong J, Ming F. Full polarimetry SAR ATR algorithm based on polarimetry similarity[J]. Foreign Electronic Measurement Technology, 29, 24-27, 46(2010).

    [11] 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, 6, 157-166(2017).

    [12] 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, 219, 130-143(2017).

    [13] Hao Y, Bai Y P, Zhang X F. Synthetic aperture radar target recognition based on KNN[J]. Fire Control & Command Control, 43, 111-113, 118(2018).

    [14] Liu H C, Li S T. Decision fusion of sparse representation and support vector machine for SAR image target recognition[J]. Neurocomputing, 113, 97-104(2013).

    [15] Thiagarajan J J, Ramamurthy K N, Knee P 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, Cypru, 1-4(2010).

    [16] 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, 54, 4806-4817(2016).

    [17] Li S, Wei Z H, Zhang B C et al. Target recognition using the transfer learning-based deep convolutional neural networks for SAR images[J]. Journal of University of Chinese Academy of Sciences, 35, 75-83(2018).

    [18] Huan R, Pan Y. Decision fusion strategies for SAR image target recognition[J]. IET Radar, Sonar & Navigation, 5, 747-755(2011).

    [19] Liu S K, Yang J. Target recognition in synthetic aperture radar images via joint multifeature decision fusion[J]. Journal of Applied Remote Sensing, 12, 016012(2018).

    [20] Ji S, Dunson D, Carin L. Multitask compressive sensing[J]. IEEE Transactions on Signal Processing, 57, 92-106(2009).

    [21] Zhang H C, Nasrabadi N M, Zhang Y N et al. Multi-view automatic target recognition using joint sparse representation[J]. IEEE Transactions on Aerospace and Electronic Systems, 48, 2481-2497(2012).

    [22] Cai D R, Zhang T. SAR target recognition based on joint use of multi-resolution representations[J]. Journal of Electronic Measurement and Instrumentation, 32, 71-77(2018).

    [23] Xu M E, Xie B L, Xu G M. Hyperspectral image super-resolution method based on spatial spectral joint sparse representation[J]. Laser & Optoelectronics Progress, 55, 071014(2018).

    [24] Li A G, Wang B N. The concept of a new nonlinear correlation information entropy and its properties and applications[J]. Information and Control, 40, 401-407, 412(2011).

    [25] Wang H D, Yao X. Objective reduction based on nonlinear correlation information entropy[J]. Soft Computing, 20, 2393-2407(2016).

    [26] Zhang X H, Hao R F, Li T Y. Hyperspectral abnormal target detection based on low rank and sparse matrix decomposition-sparse representation[J]. Laser & Optoelectronics Progress, 56, 042801(2019).

    Hong Zhang, Xinlan Zuo, Yao Huang. Feature Selection Based on the Correlation of Sparse Coefficient Vectors with Application to SAR Target Recognition[J]. Laser & Optoelectronics Progress, 2020, 57(14): 141029
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