References
- El-Darymli K, Gill EW, 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
- 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
- Amoon M, Rezai-rad GA. Automatic target recognition of synthetic aperture radar (SAR) images based on optimal selection of Zernike moment features. IET Comput Vis. 2014;8(2):77–85. doi: 10.1049/iet-cvi.2013.0027
- Clemente C, Pallotta L, Gaglione D, et al. Automatic target recognition of military vehicles with Krawtchouk moments. IEEE Trans Aerosp Electron Syst. 2017;53(1):493–500. doi: 10.1109/TAES.2017.2649160
- 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
- Anagnostopoulos GC. SVM-based target recognition from synthetic aperture radar images using target region outline descriptors. Nonlinear Anal. 2009;71(2):2934–2939. doi: 10.1016/j.na.2009.07.030
- Papson S, Narayanan RM. 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. Proceedings of the IEEE TENCON; 2008. p. 1–6.
- Cui Z, Cao Z, Yang J, et al. Target recognition in synthetic aperture radar via non-negative matrix factorization. IET Radar Sonar Navig. 2015;9(9):1376–1385. doi: 10.1049/iet-rsn.2014.0407
- 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
- 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(2):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
- Gerry MJ, Potter LC, Gupta IJ, et al. A parametric model for synthetic aperture radar measurements. IEEE Trans Antennas Propag. 1999;47(7):1179–1188. doi: 10.1109/8.785750
- Potter LC, Mose RL. Attributed scattering centers for SAR ATR. IEEE Trans Image Process. 1997;6(1):79–91. doi: 10.1109/83.552098
- Chiang HC, Moses RL, Potter LC. Model-based classification of radar images. IEEE Trans Inf Theory. 2000;46(5):1842–1854. doi: 10.1109/18.857795
- Ding B, Wen G, Zhong J, et al. Robust method for the matching of attributed scattering centers with application to synthetic aperture radar automatic target recognition. J Appl Remote Sens. 2016;10(1):016010. doi: 10.1117/1.JRS.10.016010
- 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 Sens. 2017;10(7):3334–3347. doi: 10.1109/JSTARS.2017.2671919
- Zhao Q, Principe JC. Support vector machines for synthetic aperture radar automatic target recognition. IEEE Trans Aerosp Electron Syst. 2001;37(2):643–654. doi: 10.1109/7.937475
- Sun Y, Liu Z, and 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. Proceedings of the 4th International Symposium on Communications, Control and Signal Process; 2010. p. 1–4.
- Song H, Ji K, Zhang Y, et al. Sparse representation-based SAR image target classification on the 10-class MSTAR data set. Applied Sci. 2016;6(1):26. doi: 10.3390/app6010026
- Srinivas U, Monga V, Raj RG. SAR automatic target recognition using discriminative graphical models. IEEE Trans Aerosp Electron Syst. 2014;50(1):591–606. doi: 10.1109/TAES.2013.120340
- 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
- Wagner AS. 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
- 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.
- 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.
- Dong G, Kuang G. Classification on the monogenic scale space: application to target recognition in SAR image. IEEE Trans Image Process. 2015;24(8):2527–2539. doi: 10.1109/TIP.2015.2421440
- Ding B, Wen G. Sparsity constraint nearest subspace classifier for target recognition of SAR images. J Visual Commun. Image Represent. 2018;52:170–176. doi: 10.1016/j.jvcir.2018.02.012
- Sim DG, Kwon OK, Park RH. Object matching algorithm using robust Hausdorff distance measures. IEEE Trans Image Process. 1999;8(3):425–429. doi: 10.1109/83.748897
- Zhu H. Robust and fast Hausdorff distance for image matching. Opt Eng. 2012;51(1):017203. doi: 10.1117/1.OE.51.1.017203
- Mount DM, Netanyahu NS, Moigne JL. Improved algorithms for robust point pattern matching and applications to image registration. Proceedings of the 14th Annual Symposium on Computational Geometry; 1998. p. 155–164.
- Xu D. A unified approach to autofocus and alignment for pattern localization using hybrid weighted Hausdorff distance. Pattern Recognit Lett. 2011;32(14):1747–1755. doi: 10.1016/j.patrec.2011.07.007
- Wang Y, Chua CS. Robust face recognition from 2D and 3D images using structural Hausdorff distance. Image Vis Comput. 2006;24(2):176–185. doi: 10.1016/j.imavis.2005.09.025
- Dungan KE, Potter LC. Classifying transformation-variant attributed point patterns. Pattern Recognit. 2010;43:3805–3816. doi: 10.1016/j.patcog.2010.05.033
- Huttenlocher DP, Klanderman GA, Rucklidge WJ. Comparing images using the Hausdorff distance. IEEE Trans Pattern Anal Mach Intell. 1993;15:850–863. doi: 10.1109/34.232073
- Gonzalez R, Woods R. Digital image processing. Princeton (NJ): Prentice Hall; 2008.
- Doo S, Smith G, Baker C. Target classification performance as a function of measurement uncertainty. Proceedings of the IEEE APSAR; 2015. p. 587–590.
- Ravichandran B, Gandhe A, Simith R, et al. Robust automatic target recognition using learning classifier systems. Inform Fusion. 2007;8:252–265. doi: 10.1016/j.inffus.2006.03.001
- Bhanu B, Lin Y. Stochastic models for recognition of occluded targets. Pattern Recognit. 2003;36:2855–2873. doi: 10.1016/S0031-3203(03)00182-1
- Ding B, Wen G. Exploiting multi-view SAR images for robust target recognition. Remote Sens. 2017;9:1150. doi: 10.3390/rs9111150