Open Access
Issue
ITM Web Conf.
Volume 12, 2017
The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
Article Number 05001
Number of page(s) 8
Section Session 5: Information Processing Methods and Techniques
DOI https://doi.org/10.1051/itmconf/20171205001
Published online 05 September 2017
  1. Q. Zhao and J. C. Principe 2001. “Support vector machines for sar automatic target recognition,“ IEEE Transactions on Aerospace & Electronic Systems, vol. 37, no. 2, pp. 643–654. [CrossRef] [Google Scholar]
  2. Guo G., Wang H., Bell D., Bi Y., & Greer K. 2003. Knn model-based approach in classification. Lecture Notes in Computer Science, 2888, 986–996. [CrossRef] [Google Scholar]
  3. Y. Sun, Z. Liu, S. Todorovic, and J. Li 2007. “Adaptive boosting for sar automatic target recognition,“ IEEE Transactions on Aerospace & Electronic Systems, vol. 43, no. 1, pp. 112–125. [CrossRef] [Google Scholar]
  4. A. Krizhevsky, I. Sutskever, and G. Hinton 2012. “ImageNet classification with deep convolutional neural networks,” in Proc. Adv. Neural Inf. Process. Syst., pp. 1106–1114. [Google Scholar]
  5. C. Szegedy et al. 2015. “Going deeper with convolutions,” in Proc. IEEE Computer Vision Pattern Recognition, Boston, MA, USA, Jun. 8–10, pp. 1–9. [Google Scholar]
  6. K. Simonyan and A. Zisserman 2015. “Very deep convolutional networks for large-scale image recognition,” presented at the Int. Conf. Learning Representations. [Online]. Available: http://arxiv.org/abs/1409.1556 [Google Scholar]
  7. R. Girshick, J. Donahue, T. Darrell, and J. Malik 2014. “Rich feature hierarchies for accurate object detection and semantic segmentation,“ in Proc. IEEE Computer Vision Pattern Recognition., pp. 580–587. [Google Scholar]
  8. X. Chen, S. Xiang, C. Liu, and C. Pan 2014. “Vehicle detection in satellite images by hybrid deep convolutional neural networks,“ IEEE Geoscience Remote Sense Letter, vol. 11, no. 10, pp. 1797–1801. [CrossRef] [Google Scholar]
  9. Du K., Deng Y., Wang R., Zhao T., & Li N 2016. Sar atr based on displacement- and rotation-insensitive cnn. Remote Sensing Letters, 7(9), 895–904. [CrossRef] [Google Scholar]
  10. Chen S., Wang H., Xu F., & Jin Y. Q. 2016. Target classification using the deep convolutional networks for sar images. IEEE Transactions on Geoscience & Remote Sensing, 54(8), 1–12. [NASA ADS] [CrossRef] [EDP Sciences] [Google Scholar]
  11. Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner 1998. “Gradient-based learning applied to document recognition,” Proc. IEEE, vol. 86, no. 11, pp. 2278–2324. [CrossRef] [Google Scholar]
  12. V. Nair and G. E. Hinton 2010. “Rectified linear units improve restricted boltzmann machines,” in ICML. [Google Scholar]
  13. Y. LeCun, K. Kavukcuoglu, and C. Farabet 2010. “Convolutional networks and applications in vision,“ in Proc. IEEE International Symposium Circuits System, pp. 253–256. [Google Scholar]
  14. Y. L. Cun, B. Boser, J. S. Denker, R. E. Howard, W. Habbard, L. D. Jackel, and D. Henderson 1990. “Handwritten digit recognition with a backpropagation network,“ in Advances in Neural Information Processing Systems, pp. 396–404. [EDP Sciences] [Google Scholar]
  15. C. M. Bishop 2006 Pattern Recognition and Machine Learning. New York, NY, USA: Springer-Verlag. [Google Scholar]
  16. K. He, X. Zhang, S. Ren, & J. Sun 2015. Deep residual learning for image recognition. 770–778. [CrossRef] [EDP Sciences] [PubMed] [Google Scholar]
  17. D. P. Kingma and J. Ba 2014. “Adam: A method for stochastic optimization,” Computer Science. [Google Scholar]
  18. K. He, X. Zhang, S. Ren, and J. Sun 2015. “Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification,” in Proc. Int. Conf. Comput. Vis., pp. 1026–1034. [Google Scholar]
  19. Lin M., Chen Q., & Yan S. 2013. Network in network. Computer Science. [Google Scholar]
  20. Y Bengio, Nicolas Boulanger-Lewandowski, Razvan Pascanu 2013. “Advances in optimizing recurrent networks,“ IEEE International Conference on Acoustics, Speech and Signal Processing, 8624–8628. [Google Scholar]
  21. Sun Y., Liu Z., Todorovic S., and Li J. 2007. “Adaptive boosting for sar automatic target recognition,” Aerospace and Electronic Systems, IEEE Transactions on 43, 112–125. [Google Scholar]
  22. Mishra A. K., & Motaung T. 2015. Application of linear and nonlinear PCA to SAR ATR. Radioelektronika. IEEE. pp. 349–354. [Google Scholar]
  23. D. Ganggang, W. Na, and K. Gangyao 2014. “Sparse representation of monogenic signal: With application to target recognition in sar images,” vol. 21, no. 8, pp. 952–956. [Google Scholar]
  24. S. Umamahesh, M. Vishal, and R. Raghu, G. 2014. “Sar automatic target recognition using discriminative graphical models,” vol. 50, pp. 591–606. [Google Scholar]
  25. D. Jun, C. Bo, L. Hongwei, and H. Mengyuan 2016. “Convolutional neural network with data augmentation for sar target recognition,” in IEEE Geoscience and remote sensing letters, vol. 13, no. 3, pp. 364–368. [Google Scholar]

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