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The Scientific World Journal
Volume 2014, Article ID 834140, 10 pages
http://dx.doi.org/10.1155/2014/834140
Research Article

Sparse Representation Based SAR Vehicle Recognition along with Aspect Angle

College of Electronic Science and Engineering, National University of Defense Technology, Changsha, Hunan 410073, China

Received 8 December 2013; Accepted 13 February 2014; Published 9 April 2014

Academic Editors: J.-M. Guo and Z. Hou

Copyright © 2014 Xiangwei Xing et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Citations to this Article [6 citations]

The following is the list of published articles that have cited the current article.

  • Zhao-Tong Zhu, Shi-Bao Peng, Jia Xu, and Xiao-Mei Xu, “Fast model-based automatic target recognition method for Synthetic Aperture Sonar image,” Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, vol. 37, no. 7, pp. 1757–1762, 2015. View at Publisher · View at Google Scholar
  • Vladimir Fursov, Denis Zherdev, and Nikolay Kazanskiy, “Support subspaces method for synthetic aperture radar automatic target recognition,” International Journal of Advanced Robotic Systems, vol. 13, no. 5, pp. 1–11, 2016. View at Publisher · View at Google Scholar
  • Haibo Song, Kefeng Ji, Yunshu Zhang, Xiangwei Xing, and Huanxin Zou, “Sparse Representation-Based SAR Image Target Classification on the 10-Class MSTAR Data Set,” Applied Sciences, vol. 6, no. 1, pp. 26, 2016. View at Publisher · View at Google Scholar
  • Feng-bin Zheng, and Yang Liu, “Object-oriented and multi-scale target classification and recognition based on hierarchical ensemble learning,” Computers and Electrical Engineering, vol. 62, pp. 538–554, 2017. View at Publisher · View at Google Scholar
  • Xiaohui Zhao, Yicheng Jiang, and Tania Stathaki, “Automatic Target Recognition Strategy for Synthetic Aperture Radar Images Based on Combined Discrimination Trees,” Computational Intelligence and Neuroscience, vol. 2017, pp. 1–18, 2017. View at Publisher · View at Google Scholar
  • Evgeny Minaev, Denis Zherdev, Nikolay Kazanskiy, and Vladimir Fursov, “Support subspaces method for recognition of the synthetic aperture radar images using fractal compression,” International Journal of Advanced Robotic Systems, vol. 14, no. 5, 2017. View at Publisher · View at Google Scholar