Volume 5 Issue 3
Jun.  2016
Turn off MathJax
Article Contents
Article Navigation >  Journal of Radars  > 2016  >  5(3): 320-325
Tian Zhuangzhuang, Zhan Ronghui, Hu Jiemin, Zhang Jun. SAR ATR Based on Convolutional Neural Network[J]. Journal of Radars, 2016, 5(3): 320-325. doi: 10.12000/JR16037
Citation: Tian Zhuangzhuang, Zhan Ronghui, Hu Jiemin, Zhang Jun. SAR ATR Based on Convolutional Neural Network[J]. Journal of Radars, 2016, 5(3): 320-325. doi: 10.12000/JR16037
shu

SAR ATR Based on Convolutional Neural Network

DOI: 10.12000/JR16037

  • ATR Key Laboratory, National University of Defense Technology, Changsha 410073, China

Funds:

The National Natural Science Foundation of China (61471370)

  • Received Date: 2016-02-03
  • Rev Recd Date: 2016-03-15
  • Publish Date: 2016-06-28
    Abstract
  • This study presents a new method of Synthetic Aperture Radar (SAR) image target recognition based on a convolutional neural network. First, we introduce a class separability measure into the cost function to improve this network's ability to distinguish between categories. Then, we extract SAR image features using the improved convolutional neural network and classify these features using a support vector machine. Experimental results using moving and stationary target acquisition and recognition SAR datasets prove the validity of this method.

     

    • FullText(HTML)
  • loading
    • References(18)
  • [1]
    Ross T D, Worrell S W, Velten V J, et al.. Standard SAR ATR evaluation experiments using the MSTAR public release data set[C]. Aerospace/Defense Sensing and Controls, International Society for Optics and Photonics, 1998: 566-573.
    [2]
    Tao W, Xi C, Xiangwei R, et al.. Study on SAR target recognition based on support vector machine[C]. 2009 2nd Asian-Pacific Conference on Synthetic Aperture Radar, 2009: 856-859.
    [3]
    王璐, 张帆, 李伟, 等. 基于 Gabor 滤波器和局部纹理特征提取的 SAR 目标识别算法[J]. 雷达学报, 2015, 4(6): 658-665. DOI: 10.12000/JR15076. Wang Lu, Zhang Fan, Li Wei, et al.. A method of SAR target recognition based on Gabor filter and local texture feature extraction[J]. Journal of Radars, 2015, 4(6); 658-665. DOI: 10.12000/JR15076.
    [4]
    齐会娇, 王英华, 丁军, 等. 基于多信息字典学习及稀疏表示的SAR目标识别[J]. 系统工程与电子技术, 2015, 37(6): 1280-1287. Qi Huijiao, Wang Yinghua, Ding Jun, et al.. SAR target recognition based on multi-information dictionary learning and sparse representation[J]. Systems Engineering and Electronics, 2015, 37(6): 1280-1287.
    [5]
    Hinton G E and Salakhutdinov R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507.
    [6]
    Hinton G E, Osindero S, and Teh Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554.
    [7]
    Vincent P, Larochelle H, Lajoie I, et al.. Stacked denoisingautoencoders: learning useful representations in a deep network with a local denoisingcriterion[J]. The Journal of Machine Learning Research, 2010, 11: 3371-3408.
    [8]
    Lecun Y, Bottou L, Bengio Y, et al.. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
    [9]
    Ma Y, He J, Wu L, et al.. An effective face verification algorithm to fuse complete features in convolutional neural network[C]. MultiMedia Modeling. Springer International Publishing, 2016: 39-46.
    [10]
    Ijjina E P and Mohan C K. Human action recognition based on motion capture information using fuzzy convolution neural networks[C]. 2015 Eighth International Conference on Advances in Pattern Recognition (ICAPR), Kalkata, 2015: 1-6.
    [11]
    Ciompi F, de Hoop B, van Riel S J, et al.. Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box[J]. Medical Image Analysis, 2015, 26(1): 195-202.
    [12]
    尹宝才, 王文通, 王立春. 深度学习研究综述[J]. 北京工业大学学报, 2015, 41(1): 48-59. Yin Bao-cai, Wang Wen-tong, and Wang Li-chun. Review of deep learning[J]. Journal of Beijing University of Technology, 2015, 41(1): 48-59.
    [13]
    Lecun Y, Bengio Y, and Hinton G. Deep learning[J]. Nature, 2015, 521(7553): 436-444.
    [14]
    Rumelhart D E, Hinton G E, and Williams R J. Learning representations by back-propagating errors[J]. Nature, 1986, 323(6088): 533-536.
    [15]
    孙艳丰, 齐光磊, 胡永利, 等. 基于改进 Fisher 准则的深度卷积神经网络识别算法[J]. 北京工业大学学报, 2015, 41(6): 835-841. Sun Yanfeng, Qi Guanglei, Hu Yongli, et al.. Deep convolution neural network recognition algorithm based on improved fisher criterion[J]. Journal of Beijing University of Technology, 2015, 41(6): 835-841.
    [16]
    Cortes C and Vapnik V. Support-vector networks[J]. Machine Learning, 1995, 20(3): 273-297.
    [17]
    孙即祥, 等. 现代模式识别[M]. 北京: 高等教育出版社, 2008: 624-625. Sun Jixiang, et al.. Pattern Recognition[M]. Beijing: Higher Education Press, 2008: 624-625.
    [18]
    Glorot X and Bengio Y. Understanding the difficulty of training deep feedforward neural networks[J]. Journal of Machine Learning Research, 2010, 9: 249-256.
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索
    Get Citation
    PDF
    XML
    Article views(6704) PDF downloads(3113) Cited by()
    Proportional views
    Related

    京ICP备20021838号-8   Supported by: Beijing Renhe Information Technology Co. Ltd     百度统计

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return