Abstract
In this paper, an object recognition approach for synthetic aperture radar (SAR) images is addressed, which is based on the enhanced kernel sparse representation of monogenic signal. It consists of two main modules. In the first module, to capture the spatial and spectral properties of a target at the same time, a multi-scale monogenic feature extraction scheme is proposed. In the second module, an enhanced kernel sparse representation-based classifier (KSRC) is designed. Different from the traditional KSRC, in the enhanced KSRC, we first integrate the kernel principal component analysis (KPCA) as well as the kernel fisher discriminant analysis (KFDA) to generate an augmented pseudo-transformation matrix. Then, a new discriminative feature mapping approach is presented by exploiting the augmented pseudo-transformation matrix so that the dimensionality of the kernel feature space can be effectively reduced. At last, the ℓ1 -norm minimization is utilized to calculate the sparse coefficients for a test sample, and thus the inference can be reached in terms of the total reconstruction error. Experimental results on the public moving and stationary target acquisition and recognition dataset (MSTAR) demonstrate that the proposed method achieves high recognition accuracy for SAR automatic target recognition.
Similar content being viewed by others
References
Keydel ER, Lee SW, Moore JT (Jun. 1996) MSTAR extended operating conditions: a tutorial. Proc SPIE 2757:228–242
López-Martínez C, Pottier E (2007) Coherence estimation in synthetic aperture radar data based on speckle noise modeling. Appl Opt 46(4):544–558
Qi F, Ocket I, Schreurs D, Nauwelaers B (2012) A system-level simulator for indoor mmW SAR imaging and its applications. Opt Express 20(21):23811–23820
Zhang H, Nasrabadi NM, Zhang Y, Huang TS (2012) Multi-view automatic target recognition using joint sparse representation. IEEE Trans Aero Elec Sys 48(3):2481–2497
Ross TD, Worrell SW, Velten VJ, Mossing JC, Bryant ML (1998) Standard SAR ATR evaluation experiments using the MSTAR public release data set. Proc SPIE 3370:566–573
Huan R, Pan Y (2011) Decision fusion strategies for SAR image target recognition. IET Radar, Sonar Nav 5(7):747–755
Owirka GJ, Verbout SM, Novak LM (1999) Template-based SAR ATR performance using different image enhancement techniques. Proc SPIE 3721:302–319
Casasent D, Nehemiah A (2006) Confuser rejection performance of EMACH filters for MSTAR ATR. Proc SPIE 6245:62450D
Liu M, Wu Y, Zhang Q, Wang F, Li M (2016) Synthetic aperture radar target configuration recognition using locality-preserving property and the gamma distribution. IET Radar, Sonar Nav 10(2):256–263
Park J, Kim K (2014) Modified polar mapping classifier for SAR automatic target recognition. IEEE Trans Aero Elec Sys 50(2):1092–1107
DeVore MD, O’Sullivan JA (2004) Quantitative statistical assessment of conditional models for synthetic aperture radar. IEEE Trans Image Process 13(2):113–125
Zhou J, Shi Z, Cheng X, Fu Q (2011) Automatic target recognition of SAR images based on global scattering center model. IEEE Trans Geosci Remote Sens 49(10):3713–3729
Akbarizadeh G (2012) A new statistical-based kurtosis wavelet energy feature for texture recognition of SAR images. IEEE Trans Geosci Remote Sens 50(11):4358–4368
Liu X, Huang Y, Pei J, Yang J (2014) Sample discriminant analysis for SAR ATR. IEEE Geosci Remote Sens Letters 11(12):2120–2124
Cui Z, Cao Z, Yang J, Feng J, Ren H (2015) Target recognition in synthetic aperture radar images via non-negative matrix factorisation. IET Radar, Sonar Nav 9(9):1376–1385
Amoon M, Rezai-rad G (2014) Automatic target recognition of synthetic aperture radar (SAR) images based on optimal selection of Zernike moments features. IET Comput Vis 8(2):77–85
Srinivas U, Monga V, Raj RG (2014) SAR automatic target recognition using discriminative graphical models. IEEE Trans Aero Elec Sys 50(1):591–606
Zhao Q, Principe J (2001) Support vector machines for SAR automatic target recognition. IEEE Trans Aero Elec Sys 37(2):643–654
Sun Y, Liu Z, Todorovic S, Li J (2007) Adaptive boosting for SAR automatic target recognition. IEEE Trans Aero Elec Sys 43(1):112–125
Yang S, Ma Y, Wang M (2013) Compressive feature and kernel sparse coding-based radar target recognition. IET Radar, Sonar Nav 7(7):755–763
Dong G, Wang N, Kuang G (2014) Sparse representation of monogenic signal: with application to target recognition in SAR images. IEEE Signal Process Letters 21(8):952–956
Felsberg M, Sommer G (2001) The monogenic signal. IEEE Trans Signal Process 49(12):3136–3144
Felsberg M, Sommer G (2004) The monogenic scale-space: a unifying approach to phase-based image processing in scale-space. J Math Imag Vis 21(1–2):5–26
Dong G, Kuang G, Zhao L, Lu J, Lu M (2014) Joint sparse representation of monogenic components: with application to automatic target recognition in SAR imagery. Proc IEEE Symposium on Geoscience and Remote Sensing 8(7):549–552
Gao S, Tsang IW-H, Chia L-T (2013) Sparse representation with kernels. IEEE Trans Image Process 22(2):423–434
Zhang L, Zhou W-D, Chang P-C, Liu J, Yan Z (2012) Kernel sparse representation-based classifier. IEEE Trans Signal Process 60(4):1684–1695
Schölkopf B, Smola AJ, Müller K-R (1998) Nonlinear component analysis as a kernel eigenvalue problem. Neural Comput 10(5):1299–1319
Mika S, Rätsch G, Weston J, Schölkopf B, Müller K-R (1999) Fisher discriminant analysis with kernels. In: IEEE Int. workshop neural Netw. Signal process. IX, pp 41–48
Yang M, Zhang L, Shiu SC-K, Zhang D (2012) Monogenic binary coding: an efficient local feature extraction approach to face recognition. IEEE Trans Inf Forensics Security 7(6):1738–1751
Dong G, Kuang G (2015) Classification on the monogenic scale space: application to target recognition in SAR image. IEEE Trans Image Process 24(8):2527–2539
Dong G, Kuang G, Wang N, Zhao L, Lu J (2015) SAR target recognition via joint sparse representation of monogenic signal. IEEE J Sel Topics Appl Earth Observ Remote Sens 8(7):3316–3328
Mossing JC, Ross TD (1998) An evaluation of SAR ATR algorithm performance sensitivity to MSTAR extended operating conditions. Proc SPIE 3370:554–565
Acknowledgements
This work was supported by the National Natural Science Foundation of China (Grant No. 61603124, 61871218, 61801211, 61501233), Funding of Jiangsu Innovation Program for Graduate Education (Grant No. KYLX15_0278), Fundamental Research Funds for the Central Universities (Grant No. 2019B15314, 3082017NP2017421), and the Aeronautical Science Foundation of China (Grant No. 20152052026).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ning, C., Liu, W., Zhang, G. et al. An Object Recognition Approach for Synthetic Aperture Radar Images. Mobile Netw Appl 26, 1259–1266 (2021). https://doi.org/10.1007/s11036-019-01341-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11036-019-01341-4