Target recognition method based on deep belief network

doi:10.11772/j.issn.1001-9081.2014.11.3314

Journal of Computer Applications ›› 2014, Vol. 34 ›› Issue (11): 3314-3317.DOI: 10.11772/j.issn.1001-9081.2014.11.3314

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Target recognition method based on deep belief network

SHI Hehuan¹,XU Yuelei¹,YANG Zhijun²,LI Shuai¹,LI Yueyun¹

1. Institute of Aeronautics and Astronautics Engineering, Air Force Engineering University, Xi'an Shaanxi 710038, China;
2. The No. 66350 Army of PLA, Baoding Hebei 071000, China

Received:2014-05-26 Revised:2014-07-01 Online:2014-12-01 Published:2014-11-01
Contact: SHI Hehuan

基于深度置信网络的目标识别方法

史鹤欢¹,许悦雷¹,杨志军²,李帅¹,李岳云¹

1. 空军工程大学航空航天工程学院, 西安 710038;
2. 解放军 66350部队,河北保定 071000

通讯作者: 史鹤欢
作者简介:
史鹤欢(1990-),男,陕西兴平人,硕士研究生,主要研究方向:图像处理、目标识别;许悦雷(1975-),男,河北辛集人,副教授,〖BP(〗硕士研究生导师,〖BP)〗主要研究方向:图像处理、目标识别;杨志军(1975-),男,河北高碑店人,工程师,主要研究方向:图像处理、目标识别;李帅(1988-),男,河南新乡人,硕士研究生,主要研究方向:图像处理、目标识别;李岳云(1991-),男,湖南岳阳人,硕士研究生,主要研究方向:图像处理、模式识别。

Abstract

Abstract:

Aiming at improving the robustness in pre-processing and extracting features sufficiently for Synthetic Aperture Radar (SAR) images, an automatic target recognition algorithm for SAR images based on Deep Belief Network (DBN) was proposed. Firstly, a non-local means image despeckling algorithm was proposed based on Dual-Tree Complex Wavelet Transformation (DT-CWT); then combined with the estimation of the object azimuth, a robust process on original data was achieved; finally a multi-layer DBN was applied to extract the deeply abstract visual information as features to complete target recognition. The experiments were conducted on three Moving and Stationary Target Acquisition and Recognition (MSTAR) databases. The results show that the algorithm performs efficiently with high accuracy and robustness.

摘要：

针对合成孔径雷达图像预处理鲁棒性不足、特征提取及利用不充分等问题,提出了一种基于深度置信网络的合成孔径雷达(SAR)图像目标自动识别算法。首先提出一种基于双树复小波变换（DT-CWT）的非局部均值图像降斑算法,并结合目标方位角估计实现对原始数据鲁棒的预处理;最后,引入多层深度置信网络提取针对合成孔径雷达目标的深度抽象视觉信息作为特征并完成识别任务。采用3类运动与静止目标的获取与识别(MSTAR)实测数据进行的仿真实验结果表明,所提算法具有较高鲁棒性和识别率。

CLC Number:

TP391.41

SHI Hehuan XU Yuelei YANG Zhijun LI Shuai LI Yueyun. Target recognition method based on deep belief network[J]. Journal of Computer Applications, 2014, 34(11): 3314-3317.

史鹤欢许悦雷杨志军李帅李岳云. 基于深度置信网络的目标识别方法[J]. 计算机应用, 2014, 34(11): 3314-3317.

References

[1]YIN K. Study on SAR image pressing and ground target recognition technology[D].Xi'an: Xidian University,2011.(尹奎英.SAR图像处理及地面目标识别技术研究[D].西安:西安电子科技大学,2011).
[2]LIU Z, ZHUANG L, CAO Y, et al. Target recognition of SAR images using principle[J]. Systems Engineering and Electronics,2013,35(2):282-286.(刘中杰,庄丽葵,曹云峰,等.基于主元分析和稀疏表示的SAR图像目标识别[J].系统工程与电子技术,2013,35(2):282-286.)
[3]NI X. Research of classification and automatic target recognition using SAR imagery[D]. Beijing: Chinese Academy of Sciences, Institute of Electronics,2007.(倪心强.SAR图像分类与自动目标识别技术研究[D].北京:中国科学院电子研究所,2007.)
[4]HUAN R, ZHANG P, PAN Y. SAR target recognition using PCA, ICA and Gabor wavelet decision fusion[J]. Journal of Remote Sensing,2012,16(2):262-267.
[5]ZHANG H, WANG C, ZHANG B. et al. High resolution SAR target recognition[M].Beijing: Science Press,2009.(张红,王超,张波,等.高分辨SAR图像目标识别[M].北京:科学出版社,2009.)
[6]ZHONG S, LIU Y, ZHANG Y. Attention modeling for face recognition via deep learning[EB/OL].[2013-10-10]. http://mindmodeling.org/cogsci2012/papers/0453/paper0453.pdf.
[7]IQBAL M Z, GHAFOOR A, SIDDIQUI A M. Satellite image resolution enhancement using dual-tree complex wavelet transform and nonlocal means[J].IEEE Geoscience and Remote Sensing Letters,2013,10(3):451-455.
[8]KINGSBURY N G. The dual-tree complex wavelet transform: a new technique for shift invariance and directional filters[EB/OL].[2012-10-10]. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.51.1213&rep=rep1&type=pdf.
[9]BUADES A, COLL B, MOREL J M. Image denoising methods. a new nonlocal principle[J]. SIAM Review, 2010, 52(1): 113-147.
[10]BUADES A, COLL B, MOREL J M. A non-local algorithm for image denoising[C]// CVPR 2005: Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, DC: IEEE Computer Society, 2005, 2: 60-65.
[11]SALAKHUTDINOV R, HINTON G. Deep Boltzmann machines[EB/OL]. [2012-10-10]. http://www.cs.toronto.edu/~fritz/absps/dbm.pdf.
[12]SALAKHUTDINOV R, HINTON G. An efficient learning procedure for deep Boltzmann machines[EB/OL]. [2012-10-10]. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.172.7709.
[13]HINTON G. Training products of experts by minimizing contrastive divergence[J].Neural Computation, 2002, 14(8):1711-1800.

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Target recognition method based on deep belief network

基于深度置信网络的目标识别方法

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