SAR Target Recognition via Sparsity Preserving Projections

Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 246)


Feature extraction is critical in Synthetic Aperture Radar (SAR) target recognition. Principle Component Analysis (PCA) which preserves global structure and Locality Preserving Projections (LPP) which captures local structure are two typical feature extraction methods in SAR target recognition. But they both keep only one kind of space structure. To combine these two structures, a method of SAR target recognition via Sparsity Preserving Projections (SPP) is proposed in this paper. First, SPP is employed to extract features. It preserves sparse reconstruction information which contains both global and local structure. Natural discriminative information is also kept in sparse reconstruction coefficients without prior knowledge. Then, Sparse Representation based Classification (SRC) is utilized in classification because of its robustness to noise. Experimental results on MSTAR datasets demonstrate effectiveness of our method.


SAR target recognition Feature extraction Sparse representation Sparsity preserving projections 



This work is supported by the National Natural Science Foundation of China under Projects 61201271 and Specialized Research Fund for the Doctoral Program of Higher Education 20100185120021.


  1. 1.
    Guha T, Ward RK (2012) IEEE Trans Pattern Anal Mach Intell 34(8):1576–1588Google Scholar
  2. 2.
    Thiagarajan JJ, Ramamurthy KN, Knee P, Spanias A, Berisha V (2010) Sparse representations for automatic target classification in SAR images. 2010 4th international symposium on communications, control and signal processing (ISCCSP), 1–4Google Scholar
  3. 3.
    Pillai JK, Patel VM, Chellappa R, Ratha NK (2011) Secure and robust iris recognition using random projections and sparse representations. IEEE Trans Pattern Anal Mach Intell 33(9):1877–1893CrossRefGoogle Scholar
  4. 4.
    Zhang HC, Nasrabadi NM, Zhang YN, Huang TS (2012) Multi-view automatic target recognition using joint sparse representation. IEEE Trans Aerosp Electron Syst 48(3):2481–2497CrossRefGoogle Scholar
  5. 5.
    Knee P, Thiagarajan JJ, Ramamurthy KN, Spanias A (2011) SAR target classification using sparse representations and spatial pyramids. 2011 I.E. Radar Conference (RADAR), 294–298Google Scholar
  6. 6.
    Wright J, Yang A, Ganesh A, Sastry S, Ma Y (2009) Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell 31(2):210–227CrossRefGoogle Scholar
  7. 7.
    He ZG, Lu J, Kuang G (2007) A fast SAR target recognition approach using PCA features. 2007 4th international conference on image and graphics, 580–585Google Scholar
  8. 8.
    Liu M, Wu Y, Zhao Q, Gan L (2011) SAR target configuration recognition using Locality Preserving Projections. IEEE CIE Int Conf Radar 1:740–743Google Scholar
  9. 9.
    Qiao L, Chen S, Tan X (2010) Sparsity preserving projections with applications to face recognition. Pattern Recognition 43(1):331–341CrossRefMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.School of Electronic EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina

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