Volume 4 Issue 6
Dec.  2015
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Article Navigation >  Journal of Radars  > 2015  >  4(6): 674-680
HAN Ping, WANG Huan. Synthetic Aperture Radar Target Feature Extraction and Recognition Based on Improved Sparsity Preserving Projections[J]. Journal of Radars, 2015, 4(6): 674-680. doi: 10.12000/JR15068
Citation: HAN Ping, WANG Huan. Synthetic Aperture Radar Target Feature Extraction and Recognition Based on Improved Sparsity Preserving Projections[J]. Journal of Radars, 2015, 4(6): 674-680. doi: 10.12000/JR15068
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Synthetic Aperture Radar Target Feature Extraction and Recognition Based on Improved Sparsity Preserving Projections

DOI: 10.12000/JR15068
  • 1.

    (Tianjin Key Laboratory for Advanced Signal Processing, Civil Aviation University of China, Tianjin 300300, China)

  • 2.

    (Jiujiang Airport Branch, Jiangxi Airports Group Company, Jiujiang 332000, China)

Funds:

The National Natural Science Foundation of China (61571442, 61471365), The State Key Program of National Natural Science Foundation of China (61231017), The Fundamental Research Funds for the Central Universities (3122014C004)

  • Received Date: 2015-05-28
  • Rev Recd Date: 2015-09-16
  • Publish Date: 2015-12-28
    Abstract
  • We have proposed an improved Sparsity Preserving Projection (SPP) method to implement target feature extraction. It combines the SPP feature extraction using the idea of the Locality Preserving Projection (LPP) scheme to build a new objective function, which can not only maintain the relationship of sparse reconstruction between the samples but also minimize the distance between similar sample types in the projection space. Experimental results with Moving and Stationary Target Acquisition and Recognition (MSTAR) Synthetic Aperture Radar (SAR) data sets show that the average recognition rate using the proposed method is up to 97.81% without knowing the target to be azimuth, which can improve the target recognition result even further for obvious reasons. The proposed method is an effective one for SAR target recognition.

     

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    • References(15)
  • [1]
    Alpert B K. A class of bases in L2 for sparse representation of integral operators[J]. SIAM Journal on Mathematical Analysis, 1993, 24(1): 246-262.
    [2]
    Olshausen B A, Sallee P, and Lewicki M S. Learning Sparse Image Codes Using a Wavlet Pyramid Architecture[C]. Advances in Neural Information Processing Systems, 2001: 887-893.
    [3]
    邓承志, 曹汉强. 多尺度脊波字典的构造及其在图像编码中的应用[J]. 中国图象图形学报, 2009, 14(7): 1273-1278.DENG Chengzhi and CAO Hanqiang. Construction of multiscale ridgelet dictionary and its application for image coding[J]. Journal of Image and Graphics, 2009, 14(7): 1273-1278.
    [4]
    LI S T, YIN H T, and FANG L Y. Group sparse representation with dictionary learning for medical image denoising and fusion[J]. IEEE Transactions on Biomedical Engineering, 2012, 59(12): 3450-3459.
    [5]
    马路, 邓承志, 汪胜前, 等. 特征保留的稀疏表示图像去噪[J]. 计算机应用, 2013, 33(5): 1416-1419.MA Lu, DENG Chengzhi, WANG Shengqian, et al. Feature-retained image de-noising via sparse representation[J]. Journal of Computer Applications, 2013, 33(5): 1416-1419.
    [6]
    Wright J, Yang A Y, Ganesh A, et al.. Robust face recognition via sparse representation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2): 210-227.
    [7]
    胡正平, 赵淑欢. 分辨性分解块稀疏表示遮挡人脸识别算法[J]. 信号处理, 2014, 30(2): 214-220.HU Zhengping and ZHAO Shuhuan. Discriminative decomposition structure sparse representation for face recognition with occlusion[J]. Journal of Signal Processing, 2014, 30(2): 214-220.
    [8]
    刘中杰, 庄丽葵, 曹云峰, 等. 基于主元分析和稀疏表示的SAR图像目标识别[J]. 系统工程与电子技术, 2013, 35(2): 282-286.LIU Zhongjie, ZHUANG Likui, CAO Yunfeng, et al. Target recognition of SAR images using principal component analysis and sparse representation[J]. Systems Engineering and Electronics, 2013, 35(2): 282-286.
    [9]
    王燕霞, 张弓. 基于特征参数稀疏表示的SAR图像目标识别[J]. 重庆邮电大学学报(自然科学版), 2012, 24(3): 308-313.WANG Yanxia and ZHANG Gong. Target recognition in SAR images using sparse representation based on feature space[J]. Journal of Chongqing University of Posts and Telecommunications National Science Edition (Natural Science Edition), 2012, 24(3): 308-313.
    [10]
    LIU M, WU Y, and ZHANG Q, et al. Dempster-shafer fusion of multiple sparse representation and statistical property for SAR target configuration recognition[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(6): 1106-1110.
    [11]
    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(12): 1696-1701.HAN Ping and WANG Huan. Research on the synthetic aperture radar target recognition based on KPCA and sparse representation[J]. Journal of Signal Processing, 2013, 29(12): 1696-1701.
    [12]
    QIAO L S, CHEN S C, and TAN X Y. Sparsity preserving projections with applications to face recognition[J]. Pattern Recognition, 2010, 43(1): 331-341.
    [13]
    He X F and Niyogi P. Local Preserving Projections[C]. Proceedings of Conference on Advances in Neural Information Processing Systems, 2003: 153-160.
    [14]
    Figueiredo M A T, Nowak R D, and Wright S J. Gradient projection for sparse reconstruction: application to compressed sensing and other inverse problems[J]. IEEE Journals of Selected Topics in Signal Processing, 2007, 1(4): 586-597.
    [15]
    Bryant M and Garber F. SVM classifier applied to the MSTAR public data set[C]. SPIE Algorithm for Synthetic Aperture Rader Imagery, Orlando, FL, 1999, 3721: 662-672.
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