249
Views
10
CrossRef citations to date
0
Altmetric
Impending Special Issue

SAR target recognition via sparse representation of multi-view SAR images with correlation analysis

&
Pages 897-910 | Received 20 Nov 2018, Accepted 23 Jan 2019, Published online: 06 Feb 2019

References

  • El-Darymli K, Eric WG, McGuire P, et al. Automatic target recognition in synthetic aperture radar imagery: a state-of-the-art review. IEEE Access. 2016;4:6014–6058. doi: 10.1109/ACCESS.2016.2611492
  • El-Darymli K, McGuire P, Power D, et al. Target detection in synthetic aperture radar imagery: a state-of-the-art survey. J Appl Remote Sens. 2013;7(1):071598. doi: 10.1117/1.JRS.7.071598
  • Gao G. An improved scheme for target discrimination in high-resolution SAR images. IEEE Trans Geosci Remote Sen. 2011;49(1):277–294. doi: 10.1109/TGRS.2010.2052623
  • Amoon M, Rezai-rad G. Automatic target recognition of synthetic aperture radar (SAR) images based on optimal selection of Zernike moments features. IET Comput Vis. 2014;8(2):77–85. doi: 10.1049/iet-cvi.2013.0027
  • Park J, Park S, Kim K. New discrimination features for SAR automatic target recognition. IEEE Geosci Remote Sens Lett. 2013;10(3):476–480. doi: 10.1109/LGRS.2012.2210385
  • Anagnostopoulos GC. SVM-based target recognition from synthetic aperture radar images using target region outline descriptors. Nonlinear Anal. 2009;71(2):e2934–e2939. doi: 10.1016/j.na.2009.07.030
  • Papson S, Narayanan R. Classification via the shadow region in SAR imagery. IEEE Trans Aerosp Electron Syst. 2012;48(2):969–980. doi: 10.1109/TAES.2012.6178042
  • Mishra AK. Validation of PCA and LDA for SAR ATR. Proc IEEE TENCON. 2008: 1–6.
  • Cui Z, Cao Z, Yang J, et al. Target recognition in synthetic aperture radar images via non-negative matrix factorisation. IET Radar Sonar Navig. 2015;9(9):1376–1385. doi: 10.1049/iet-rsn.2014.0407
  • Yu M, Dong G, Fan H, et al. SAR target recognition via local sparse representation of multi-manifold regularized low-rank approximation. Remote Sens. 2018;10:211. doi: 10.3390/rs10020211
  • Liu X, Huang Y, Pei J, et al. Sample discriminant analysis for SAR ATR. IEEE Geosci Remote Sens Lett. 2014;11(12):2120–2124. doi: 10.1109/LGRS.2014.2321164
  • Huang Y, Pei J, Yang J, et al. Neighborhood geometric center scaling embedding for SAR ATR. IEEE Trans Aerosp Electron Syst. 2014;50(1):180–192. doi: 10.1109/TAES.2013.110769
  • Potter LC, Mose RL. Attributed scattering centers for SAR ATR. Image Process. 1997;6(1):79–91. doi: 10.1109/83.552098
  • Ding B, Wen G, Zhong J, et al. A robust similarity measure for attributed scattering center sets with application to SAR ATR. Neurocomputing. 2017;219:130–143. doi: 10.1016/j.neucom.2016.09.007
  • Ding B, Wen G, Huang X, et al. Target recognition in synthetic aperture radar images via matching of attributed scattering centers. IEEE J Sel Topics Appl Earth Observ Remote Sen. 2017;10(7):3334–3347. doi: 10.1109/JSTARS.2017.2671919
  • Zhao Q, Principe J. Support vector machines for SAR automatic target recognition. IEEE Trans Aerosp Electron Syst. 2001;37(2):643–654. doi: 10.1109/7.937475
  • Liu H, Li S. Decision fusion of sparse representation and support vector machine for SAR image target recognition. Neurocomputing. 2013;113:97–104. doi: 10.1016/j.neucom.2013.01.033
  • Sun Y, Liu Z, Todorovic S, et al. Adaptive boosting for SAR automatic target recognition. IEEE Trans Aerosp Electron Syst. 2007;43(1):112–125. doi: 10.1109/TAES.2007.357120
  • Thiagaraianm JJ, Ramamurthy KN, Knee P, et al. Sparse representations for automatic target classification in SAR images. Proc 4th Int Symp Commun. Control Signal Process. 2010: 1–4.
  • Song H, Ji K, Zhang Y, et al. Sparse representation-based SAR image target classification on the 10-class MSTAR data set. Appl Sci. 2016;6(26.
  • Zhang X, Liu Z, Liu S, et al. Sparse coding of 2D-slice Zernike moments for SAR ATR. Int J Remote Sens. 2017;38(2):412–431. doi: 10.1080/01431161.2016.1266107
  • Zhang X, Wang Y, Tan Z, et al. Two-stage multi-task representation learning for synthetic aperture radar (SAR) target images classification. Sensors. 2017;17(11):2506. doi: 10.3390/s17112506
  • Zhang X, Wang Y, Li D, et al. Fusion of Multifeature Low-Rank representation for synthetic aperture radar target Configuration recognition. IEEE Geosci Remote Sens Lett. 2018;15(9):1402–1406. doi: 10.1109/LGRS.2018.2842068
  • Chen S, Wang H, Xu F, et al. Target classification using the deep convolutional networks for SAR images. IEEE Trans Geosci Remote Sens. 2016;47(6):1685–1697.
  • Wagner SA. SAR ATR by a combination of convolutional neural network and support vector machines. IEEE Trans Aerosp Electron Syst. 2016;52(6):2861–2872. doi: 10.1109/TAES.2016.160061
  • Ding J, Chen B, Liu H, et al. Convolutional neural network with data augmentation for SAR target recognition. IEEE Geosci Remote Sens Lett. 2016;13(3):364–368.
  • Yan Y. Convolutional neural networks based on augmented training samples for synthetic aperture radar target recognition. J Electron Imaging. 2018;27(2):023024. doi: 10.1117/1.JEI.27.2.023024
  • Brown MZ. SPIE Proceedings. Proc SPIE. 2003;5095: 265–274. doi: 10.1117/12.487171
  • Huan R, Pan Y. Target recognition for multi-aspect SAR images with fusion strategies. Prog Electromagn Res. 2013;134:267–288. doi: 10.2528/PIER12100304
  • Zhang H, Nasrabadi NM, Zhang Y, et al. Multi-view automatic target recognition using joint sparse representation. IEEE Trans Aerosp Electron Syst. 2012;48(3):2481–2497. doi: 10.1109/TAES.2012.6237604
  • Cao Z, Xu L, Feng J. Automatic target recognition with joint sparse representation of heterogeneous multi-view SAR images over a locally adaptive dictionary. Signal Processing. 2016;126:127–134. doi: 10.1016/j.sigpro.2015.12.018
  • Ding B, Wen G. Exploiting multi-view SAR images for robust target recognition. Remote Sens. 2017;9:1150. doi: 10.3390/rs9111150
  • Dong G, Kuang G, Wang N, et al. SAR target recognition via joint sparse representation of monogenic signal. IEEE J Sel Topics Appl Earth Observ Remote Sen. 2015;8(7):3316–3328. doi: 10.1109/JSTARS.2015.2436694
  • Zhang Z, Liu S. Joint sparse representation for multi-resolution representations of SAR images with application to target recognition. J Electromagn Waves Appl. 2018;32(11):1342–1353. doi: 10.1080/09205071.2018.1436005
  • Tropp JA, Gilbert AC, Strauss MJ. Algorithms for simultaneous sparse approximation. EURASIP J Appl Signal Processing. 2006;86(3):589–602.
  • Ji S, Dunson D, Carin L. Multitask compressive sensing. IEEE Trans Signal Process. 2009;57(1):92–106. doi: 10.1109/TSP.2008.2005866
  • Ding B, Wen G, Huang X, et al. Target recognition in SAR images by exploiting the azimuth sensitivity. Remote Sens Lett. 2017;8(9):821–830. doi: 10.1080/2150704X.2017.1331052
  • Gonzalez R, Woods R. Digital image Processing. Princeton (NJ): Prentice Hall; 2008.
  • Wright J, Yang A, Ganesh A, et al. Robust face recognition via sparse representation. IEEE Trans Pattern Anal Mach Intell. 2009;31(2):210–227. doi: 10.1109/TPAMI.2008.79
  • Ding B, Wen G, Ma C, et al. Target recognition in synthetic aperture radar images using binary morphological operations. J Appl Remote Sens. 2016;10(4):046006. doi: 10.1117/1.JRS.10.046006
  • Doo S, Smith G, Baker C. Target classification performance as a function of measurement uncertainty. Proc IEEE APSAR. 2015: 587–590.
  • Bhanu B, Lin Y. Stochastic models for recognition of occluded targets. Pattern Recognition. 2003;36:2855–2873. doi: 10.1016/S0031-3203(03)00182-1

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.