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ISSN (Print): 2352-0965
ISSN (Online): 2352-0973

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Research Article

Target Recognition in SAR Image by Joint Classification of Target Region and Shadow

Author(s): Yongpeng Tao*, Yu Jing and Cong Xu

Volume 12, Issue 4, 2019

Page: [347 - 354] Pages: 8

DOI: 10.2174/2352096511666180727110642

open access plus

Abstract

Background: A synthetic aperture radar (SAR) automatic target recognition (ATR) method is proposed in this paper via the joint classification of the target region and shadow.

Methods: The elliptical Fourier descriptors (EFDs) are used to describe the target region and shadow extracted from the original SAR image. In addition, the relative positions between the target region and shadow are represented by a constructed feature vector. The three feature vectors complement each other to provide more comprehensive descriptions of the target’s physical properties, e.g., sizes and shape. In the classification stage, the three feature vectors are jointly classified based on the joint sparse representation (JSR). JSR is a multi-task learning algorithm, which can not only represent each component properly but also exploit the inner correlations of different components. Finally, the target type is determined to the class with the minimum reconstruction error.

Results: Experiments have been conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. The proposed method achieves a high recognition accuracy of 96.86% for 10-class recognition problem under the standard operating condition (SOC). Moreover, robustness of the proposed method is also superior over the reference methods under the extended operating conditions (EOCs) like configuration variance, depression angle variance, and noise corruption.

Conclusion: Therefore, the effectiveness and robustness of the proposed method can be quantitatively demonstrated by the experimental results.

Keywords: Synthetic Aperture Radar (SAR), Automatic Target Recognition (ATR), target region, shadow, Joint Sparse Representation (JSR), MSTAR.

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[1]
E. El-Khalid, W.G. Eric, M. Peter, D. Power, and C. Moloney, "Automatic target recognition in synthetic aperture radar imagery: A state-of-the-art review", IEEE Access, vol. 4, pp. 6014-6058, 2016.
[2]
J. Park, S. Park, and K. Kim, "New discrimination features for SAR automatic target recognition", IEEE Geosci. Remote Sens. Lett., vol. 10, no. 3, pp. 476-480, 2013.
[3]
B. Ding, G. Wen, and C. Ma, "Target recognition in synthetic aperture radar images using binary morphological operations", J. Appl. Remote Sens., vol. 10, no. 4, .pp. 046006, 2016
[4]
M. Amoon, and G.A. Rezai-Rad, "Automatic target recognition of synthetic aperture radar (SAR) images based on optimal selection of Zernike moment features", IET Comput. Vis., vol. 8, pp. 77-85, 2014.
[5]
G.C. Anagnostopoulos, "SVM-based target recognition from synthetic aperture radar images using target region outline descriptors", Nonlinear Anal., vol. 71, pp. 2934-2939, 2009.
[6]
S. Papson, and R. Narayanan, "Classification via the shadow region in SAR imagery", IEEE Trans. Aerosp. Electron. Syst., vol. 48, pp. 969-980, 2012.
[7]
M. Chang, and X. You, "Target recognition in SAR images based on information-decoupled representation", Remote Sens., vol. 10, p. 138, 2018.
[8]
B. Ding, G. Wen, and C. Ma, Evaluation of target segmentation on SAR target recognition., Proceed. ICCSS, 2017, pp. 663-667.
[9]
A.K. Mishra, "Validation of PCA and LDA for SAR ATR", Proceed. IEEE TENCON. pp. 1-6, 2008.
[10]
Z. Cui, Z. Cao, and J. Yang, "Target recognition in synthetic aperture radar via non-negative matrix factorization", IET Radar Sonar & Navigation, vol. 9, pp. 1376-1385, 2015.
[11]
Y. Huang, J. Yang, B. Wang, and X. Liu, "Neighborhood geometric center scaling embedding for SAR ATR", IEEE Trans. Aerosp. Electron. Syst., vol. 5, pp. 180-192, 2014.
[12]
M. Yu, G. Dong, and H. Fan, "SAR target recognition via a local sparse representation of multi-manifold regularized low-rank approximation", Remote Sens., vol. 10, p. 211, 2018.
[13]
X. Liu, Y. Huang, and J. Pei, "Sample discriminant analysis for SAR ATR", IEEE Geosci. Remote Sens. Lett., vol. 11, pp. 2120-2124, 2014.
[14]
B. Ding, G. Wen, and J. Zhong, "Robust method for the matching of attributed scattering centers with application to synthetic aperture radar automatic target recognition", J. Appl. Remote Sens., vol. 10, .pp. 016010, 2016.
[15]
B. Ding, G. Wen, and J. Zhong, "A robust similarity measure for attributed scattering center sets with application to SAR ATR", Neurocomput., vol. 219, pp. 130-143, 2017.
[16]
B. Ding, G. Wen, and X. Huang, "Target recognition in synthetic aperture radar images via matching of attributed scattering centers", IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 10, pp. 3334-3347, 2017.
[17]
B. Ding, G. Wen, C. Ma, and X. Yang, "Decision fusion based on physically relevant features for SAR ATR", IET Radar Sonar & Navigation. vol. 11, pp. 682-690, 2017.
[18]
B. Ding, G. Wen, and X. Huang, "Data augmentation by multilevel reconstruction using attributed scattering center for SAR target recognition", IEEE Geosci. Remote Sens. Lett., vol. 14, pp. 979-983, 2017.
[19]
J. Fan, and A. Tomas, "Target recognition based on attributed scattering centers with application to robust SAR ATR", Remote Sens., vol. 10, p. 655, 2018.
[20]
Q. Zhao, and J.C. Principe, "Support vector machines for synthetic radar automatic target recognition", IEEE Trans. Aerosp. Electron. Syst., vol. 37, pp. 643-654, 2001.
[21]
H. Liu, and S. Li, "Decision fusion of sparse representation and support vector machine for SAR image target recognition", Neurocomputing, vol. 113, pp. 97-104, 2013.
[22]
J.J. Thiagaraianm, K.N. Ramamurthy, P. Knee, A. Spanias, and V. Berisha, "Sparse representations for automatic target classification in SAR images, In:", Proceed. 4th Int. Symp. Commun. Control Signal Process. Limassol, Cyprus, 2010, pp. 1-4.
[23]
H. Song, K. Ji, and Y. Zhang, "Sparse representation-based SAR image target classification on the 10-class MSTAR data set", Appl. Sci. vol. 6, pp. 1-11, 2015.
[24]
B. Ding, and G. Wen, "Sparsity constraint nearest subspace classifier for target recognition of SAR images", J. Vis. Commun. Image Represent., vol. 52, pp. 170-176, 2018.
[25]
A. Parsian, M. Ramezani, and N. Ghadimi, "A hybrid neural network-gray wolf optimization for melanoma detection", Biomed. Res., vol. 28, pp. 3408-3411, 2017.
[26]
N. Razmjooy, F.R. Sheykhahmad, and N. Ghadimi, "A hybrid neural network-world cup optimization algorithm for melanoma detection", Open Med., vol. 13, pp. 9-16, 2018.
[27]
H. Leng, X. Li, J. Zhu, H. Tang, Z. Zhang, and N. Ghadimi, "A new wind power prediction method based on ridgelet transforms, hybrid feature selection and closed loop forecasting", Adv. Eng. Inform., vol. 36, pp. 20-30, 2018.
[28]
C. Peng, J. Cheng, and Q. Cheng, "A supervised learning model for high-dimensional and large-scale data", ACM Trans. Intell. Syst. Technol., vol. 8, p. 30, 2017.
[29]
Q. Cheng, H. Zhou, J. Cheng, and H. Li, "A minimax framework for classification with application to images and high dimensional data", IEEE Trans. Pattern Anal. Mach. Intell., vol. 36, pp. 2117-2130, 2014.
[30]
S. Chen, H. Wang, F. Xu, and Y. Jin, "Target classification using the deep convolutional networks for SAR images", IEEE Trans. Geosci. Remote Sens., vol. 47, pp. 1685-1697, 2016.
[31]
J. Ding, B. Chen, and H. Liu, "Convolutional neural network with data augmentation for SAR target recognition", IEEE Geosci. Remote Sens. Lett., vol. 13, pp. 364-368, 2016.
[32]
K. Du, Y. Deng, and R. Wang, "SAR ATR based on displacement- and rotation- insensitive CNN", Remote Sens. Lett., vol. 7, pp. 895-904, 2016.
[33]
J.A. Tropp, A.C. Gilbert, and M.J. Strauss, "Algorithms for simultaneous sparse approximation", EURASIP J. Appl. Signal Process., vol. 86, pp. 589-602, 2006.
[34]
S. Ji, D. Dunson, and L. Carin, "Multitask compressive sensing", IEEE Trans. Signal Process., vol. 57, pp. 92-106, 2009.
[35]
H. Zhang, N. Nasrabadi, Y. Zhang, and T. Huang, "Multi-view automatic target recognition using joint sparse representation", IEEE Trans. Aerosp. Electron. Syst., vol. 48, . pp. 2481-2497, 2012.
[36]
G. Dong, G. Kuang, and N. Wang, "SAR target recognition via a joint sparse representation of monogenic signal", IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., vol. 8, pp. 3316-3328, 2015.
[37]
Z. Zhang, and S. Liu, "Joint sparse representation of multi-resolution representations of SAR images with application to target recognition", J. Electromagn. Waves Appl., vol. 32, pp. 1342-1353, 2018.
[38]
R.C. Gonzalez, and R.R. Woods, "Digital Image Processing”, 3rd ed., New Jersey, Prentice Hall", 2008
[39]
S. Doo, G. Smith, and C. Baker, "Target classification performance as a function of measurement uncertainty, In:", Proceed. 5th Asia- Pacific Conference on Synthetic Aperture Radar. Singapore, Singapore, 2015, pp. 587-590.
[40]
B. Ding, and G. Wen, "Exploiting multi-view SAR images for robust target recognition", Remote Sens., vol. 9, p. 1150, 2017.

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