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. [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. [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. [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]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.