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Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey

[3] O. Kechagias-Stamatis, N. Aouf, and M. A. Richardson,

[17] S. Chen and H. Wang, " SAR target recognition based on

" 3D automatic target recognition for future LIDAR mis-

deep learning, " in Proc. Int. Conf. Data Sci. Adv. Analyt-

siles, " IEEE Trans. Aerosp. Electron. Syst., vol. 52, no. 6,

ics, Oct. 2014, pp. 541-547.

pp. 2662-2675, Dec. 2016.
[4] O. Kechagias-Stamatis and N. Aouf, " Evaluating 3D local

[18] R. Paladini, M. Martorella, and F. Berizzi, " Classification
of man-made targets via invariant coherency-matrix eigen-

descriptors for future LIDAR missiles with automatic tar-

vector decomposition of polarimetric SAR/ISAR images, "

get recognition capabilities, " Imag. Sci. J., vol. 65, no. 7,

IEEE Trans. Geosci. Remote Sens., vol. 49, no. 8,

pp. 428-437, Aug. 2017.

pp. 3022-3034, Aug. 2011.

[5] O. Kechagias-Stamatis, N. Aouf, G. Gray, L. Chermak,

[19] D. Perissin and A. Ferretti, " Urban-target recognition by

M. Richardson, and F. Oudyi, " Local feature based auto-

means of repeated spaceborne SAR images, " IEEE Trans.

matic target recognition for future 3D active homing seeker

Geosci. Remote Sens., vol. 45, no. 12, pp. 4043-4058,

missiles, " Aerosp. Sci. Technol., vol. 73, pp. 309-317,
Feb. 2018.

Dec. 2007.
[20] G. Dong, N. Wang, and G. Kuang, " Sparse representation

[6] S. Chen, H. Wang, F. Xu, and Y.-Q. Q. Jin, " Target classi-

of monogenic signal: With application to target recognition

fication using the deep convolutional networks for SAR

in SAR images, " IEEE Signal Process. Lett., vol. 21,

images, " IEEE Trans. Geosci. Remote Sens., vol. 54,
no. 8, pp. 4806-4817, Aug. 2016.

no. 8, pp. 952-956, Aug. 2014.
[21] F. Zhou, L. Wang, X. Bai, and Y. Hui, " SAR ATR of

[7] Y. Zhong and G. Ettinger, " Enlightening deep neural net-

ground vehicles based on LM-BN-CNN, " IEEE Trans.

works with knowledge of confounding factors, " in Proc.
IEEE Int. Conf. Comput. Vis. Workshops, Oct. 2017,
pp. 1077-1086.

Geosci. Remote Sens., vol. 56, no. 12, pp. 7282-7293,
Dec. 2018.
[22] O. Kechagias-Stamatis and N. Aouf, " A new passive 3-D

[8] A. Profeta, A. Rodriguez, and H. S. Clouse, " Convolutional

automatic target recognition architecture for aerial

neural networks for synthetic aperture radar classification, "

platforms, " IEEE Trans. Geosci. Remote Sens., vol. 57,

Proc. SPIE, vol. 9843, May 2016, Art. no. 98430M.

no. 1, pp. 406-415, Jan. 2019.

[9] D. A. E. Morgan, " Deep convolutional neural networks for

[23] C. Belloni, A. Balleri, N. Aouf, T. J. Merlet, and J. - M. Le

ATR from SAR imagery, " Proc. SPIE, vol. 9475, 2015,

Caillec, " SAR image dataset of military ground targets

Art. no. 94750F.

with multiple poses for ATR, " Proc. SPIE, vol. 10432,

[10] X. Zhang, J. Qin, and G. Li, " SAR target classification using
Bayesian compressive sensing with scattering centers

2017, Art. no. 104320N.
[24] AFRL and DARPA, " Sensor data management system

features, " Prog. Electromagn. Res., vol. 136, pp. 385-407,

website, MSTAR database, " 1995. [Online]. Available:

2013.

https://www.sdms.afrl.af.mil/index.php?collection¼mstar

[11] S. H. Doo et al.et al., " Aspect invariant features for radar
target recognition, " IET Radar, Sonar Navig., vol. 11,

accessed May 13, 2020.
[25] N. J. S. Stacy, D. P. Badger, A. S. Goh, M. Preiss,

no. 4, pp. 597-604, Apr. 2017.

and M. L. Williams, " The DSTO Ingara airborne X-

[12] K. El-Darymli, P. McGuire, E. W. Gill, D. Power, and

Band SAR polarimetric upgrade: First results, " Int.

C. Moloney, " Holism-based features for target classification in focused and complex-valued synthetic aperture

Geosci. Remote Sens. Symp., vol. 7, no. C, pp. 4474-4476,
2003.

radar imagery, " IEEE Trans. Aerosp. Electron. Syst.,

[26] K. El-Darymli, P. McGuire, D. Power, and C. Moloney,

vol. 52, no. 2, pp. 786-808, Apr. 2016.

" Target detection in synthetic aperture radar imagery: A

[13] S. Deng, L. Du, C. Li, J. Ding, and H. Liu, " SAR automatic

state-of-the-art survey, " J. Appl. Remote Sens., vol. 7,

target recognition based on euclidean distance restricted
autoencoder, " IEEE J. Sel. Topics Appl. Earth Obs. Remote

no. 1, 2013, Art. no. 071598.
[27] K. El-Darymli, E. W. Gill, P. McGuire, D. Power, and

Sens., vol. 10, no. 7, pp. 3323-3333, Jul. 2017.

C. Moloney, " Automatic target recognition in synthetic

[14] M. Kang, K. Ji, X. Leng, X. Xing, and H. Zou, " Synthetic
aperture radar target recognition with feature fusion based

aperture radar imagery: A state-of-the-art review, " IEEE
Access, vol. 4, pp. 6014-6058, 2016.

on a stacked autoencoder, " Sensors, vol. 17, no. 12,

[28] G. Dong, G. Liao, H. Liu, and G. Kuang, " A review of the

Jan. 2017, Art. no. 192.

autoencoder and its variants: A comparative perspective

[15] Z. Cui, J. Feng, Z. Cao, H. Ren, and J. Yang, " Target rec-

from target recognition in synthetic-aperture radar images, "

ognition in synthetic aperture radar images via non-nega-

IEEE Geosci. Remote Sens. Mag., vol. 6, no. 3, pp. 44-68,

tive matrix factorisation, " IET Radar, Sonar Navig., vol. 9,
no. 9, pp. 1376-1385, Dec. 2015.

Sep. 2018.
[29] Y. Yang, Y. Qiu, and C. Lu, " Automatic target classifica-

[16] M. Wilmanski, C. Kreucher, and J. Lauer, " Modern
approaches in deep learning for SAR ATR, " Proc. SPIE,
vol. 9843, May 2016, Art. no. 98430N.

76

tion experiments on the MSTAR SAR images, " in Proc.
6th Int. Conf. Softw. Eng., Artif. Intell. Netw. Parallel/Distrib. Comput., 2005, pp. 2-7.

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