基于PGBN模型的SAR图像目标识别方法

郭丹丹; 陈渤; 丛玉来; 文伟

doi:10.11999/JEIT161068

基于PGBN模型的SAR图像目标识别方法

doi: 10.11999/JEIT161068

郭丹丹¹,
陈渤^1, ,,
丛玉来¹,
文伟¹

基金项目:

国家自然科学基金(61372132, 61271291)，新世纪优秀人才支持计划(NCET13-0945)，杰出青年科学基金(61525105)，青年千人计划，国防预研基金

计量
- 文章访问数: 2074
- HTML全文浏览量: 198
- PDF下载量: 370
- 被引次数: 0
出版历程
- 收稿日期: 2016-10-12
- 修回日期: 2016-12-02
- 刊出日期: 2016-12-19

SAR Image Recognition Method with Poisson Gamma Belief Network Model

GUO Dandan¹,
CHEN Bo^{1
, ,},
CONG Yulai¹,
WEN Wei¹

Funds:

The National Natural Science Foundation of China (61372132, 61271291), The Program for New Century Excellent Talents (NCET13-0945), The National Science Fund for Distinguished Young Scholars (61525105), The Program for Young Thousand Talent by Chinese Central Government

摘要

摘要: 特征提取是合成孔径雷达图像目标识别的关键步骤，也是难点之一。该文提出一种基于PGBN(Poisson Gamma Belief Network)模型的SAR图像目标识别方法。PGBN模型作为一种深层贝叶斯生成网络，利用伽马分布具有的高度非线性，从复杂的SAR图像数据中获得了更具结构化的多层特征表示，这种多层特征表示有效提高了SAR图像目标识别性能。为了获得更高的训练效率和识别率，该文进一步采用朴素贝叶斯准则提出了一种对PGBN模型进行分类的方法。实验采用MSTAR的3类目标数据进行了验证，结果表明通过该方法提取的特征有更好的结构信息，对SAR图像目标识别具有较好的性能。
- SAR图像 /
- 特征提取 /
- PGBN(Poisson Gamma Belief Network)模型
Abstract: Feature extraction is a key step and difficult point in SAR image target recognition. This paper presents a novel method based on Poisson Gamma Belief Network (PGBN) for SAR image target recognition. As a deep Bayesian generative network, the PGBN model obtains a more structured multi-layer feature representation from the complex SAR image data using the high nonlinearity of the Gamma distribution, and the multi-layer feature representation effectively improves SAR image target recognition performance. In order to obtain a higher recognition rate and efficiency of training, this paper further proposes a method for classifying PGBN model based on the Naive Bayes rule. The experimental results about MSTAR dataset show that the feature extracted by this new method has better structure information, and it has better performance for SAR image target recognition.
- SAR image /
- Feature extraction /
- Poisson Gamma Belief Network (PGBN) model

HTML全文

参考文献(26)

张红, 王超, 张波. 高分辨率SAR图像目标识别[M]. 北京: 科学出版社, 2009: 5.2 节.

ZHANG Hong, WANG Chao, and ZHANG Bo. High Resolution SAR Images Target Recognition[M]. Beijing: Science Press, 2009: 5.2 Section.

保铮, 邢孟道, 王彤. 雷达成像技术[M]. 北京: 电子工业出版社, 2004: 1.1 节.

BAO Zheng, XING Mengdao, and WANG Tong. Radar Imaging Technology[M]. Beijing: Publishing House of Electronics Industry, 2004: 1.1 Section.

HE Zhiguo, LU Jun, and YAO Kuanggang. A fast SAR target recognition approach using PCA features[C]. International Conference on Image and Graphics, Chengdu, China, 2007: 580-585.

LIN C, PENG F, WANG B H, et al. Research on PCA and KPCA self-fusion based MSTAR SAR automatic target recognition algorithm[J]. Journal of Electronic Science and Technology, 2012, 10(4): 352-357.

宦若虹, 杨汝良. 基于ICA和SVM的SAR图像特征提取与目标识别[J]. 计算机工程, 2008, 34(13): 24-25.

HUAN Ruohong and YANG Ruliang. SAR images feature extraction and target recognition based on ICA and SVM[J]. Computer Engineering, 2008, 34(13): 24-25.

LEE D D and SEUNG H S. Learning the parts of objects by non-negative matrix factorization[J]. Nature, 1999, 401(6755): 788-791.

LEE D D and SEUNG H S. Algorithms for non-negative matrix factorization[C]. Neural Information Processing Systems, Denver, CO, USA, 2000: 556-562.

龙泓琳, 皮亦鸣, 曹宗杰. 基于非负矩阵分解的SAR图像目标识别[J]. 电子学报, 2010, 38(6): 1425-1429.

LONH Honglin, PI Yiming, and CAO Zongjie . Non-negative matrix factorization for target recognition[J]. Acta Electronica Sinica, 2010, 38(6): 1425-1429.

ZHOU Mingyuan and CARIN Lawrence. Beta-negative binomial process and Poisson factor analysis[C]. Artificial Intelligence and Statistics. La Palma, Canary Islads, Spain, 2012: 1462-1471.

孙洪. 高分辨率SAR图像目标识别[M]. 北京: 电子工业出版社, 2013: 5.1 节.

SUN Hong. Processing of Synthetic Aperture Radar Images [M]. Beijing: Publishing House of Electronics Industry, 2013: 5.1 Section.

ZHOU Mingyuan, CONG Yulai, and CHEN Bo. The Poisson Gamma belief network[C]. Neural Information Processing Systems, Montreal, Canda, 2015: 562-570.

CHEN Y, ZHAO X, and JIA X. Spectral-Spatial classification of hyperspectral data based on deep belief network[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(6): 1-12. doi: 10.1109/JSTAR.2015.2388577.

张学峰. 雷达高分辨距离像目标识别与拒判方法研究[D]. [博士论文], 西安电子科技大学, 2016: 71-73.

ZHANG Xuefeng. Study of radar target recognition and outlier rejection based on high range resolution profiles[D]. [Ph.D. dissertation], Xi dian University, 2016: 71-73.

LIU X, LIU R, MA J, et al. Privacy-preserving patent-centric clinical decision support system on Nave Bayes classification [J]. IEEE Journal of Biomedical Health Informatics, 2016, 20(2): 655-668. doi: 10.1109/JBHI.2015.2407157.

CHAN Chihchung and LIN Chinjen. LIBSVM: A library for support vector machines[J]. ACM Transactions on Intelligent Systems and Technology, 2011, 2(3): 1-27. doi: 10.1145/ 1961189.1961199.

GE Hinton. A practical guide training restricted boltzmann machines[J]. Momentum, 2010, 9(1): 599-619. doi: 10.007/ 978-3-642-35289-8_32.

丁军, 刘宏伟, 陈渤. 相似性约束的深度置信网络在SAR图像目标识别中的应用[J]. 电子与信息学报, 2016, 38(1): 91-103. doi: 10.11999/JEIT150366.

DING Jun, LIU Hongwei, and CHEN Bo. Application of similar constraints deep belief networks in SAR image target recognition[J]. Journal of Electronics Information Technology, 2016, 38(1): 91-103. doi: 10.11999/JEIT150366.

丁军, 刘宏伟, 王英华, 等. 一种联合阴影和目标区域图像的SAR目标识别方法[J]. 电子与信息学报, 2015, 37(3): 594-600. doi: 10.11999/JEIT140713.

DING Jun, LIU Hongwei, WANG Yinghua, et al. The method of SAR target recognition with joint shadow region and target region image[J]. Journal of Electronics Information Technology, 2015, 37(3): 594-600. doi: 10.11999/ JEIT140713.

施引文献

资源附件(0)

访问统计