基于PGBN模型的SAR圖像目標(biāo)識別方法

郭丹丹; 陳渤; 叢玉來; 文偉

doi:10.11999/JEIT161068

基于PGBN模型的SAR圖像目標(biāo)識別方法

doi: 10.11999/JEIT161068

郭丹丹¹,
陳渤^1, ,,
叢玉來¹,
文偉¹

基金項(xiàng)目:

國家自然科學(xué)基金(61372132, 61271291)，新世紀(jì)優(yōu)秀人才支持計(jì)劃(NCET13-0945)，杰出青年科學(xué)基金(61525105)，青年千人計(jì)劃，國防預(yù)研基金

計(jì)量
- 文章訪問數(shù): 2174
- HTML全文瀏覽量: 219
- PDF下載量: 371
- 被引次數(shù): 0
出版歷程
- 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: 特征提取是合成孔徑雷達(dá)圖像目標(biāo)識別的關(guān)鍵步驟，也是難點(diǎn)之一。該文提出一種基于PGBN(Poisson Gamma Belief Network)模型的SAR圖像目標(biāo)識別方法。PGBN模型作為一種深層貝葉斯生成網(wǎng)絡(luò)，利用伽馬分布具有的高度非線性，從復(fù)雜的SAR圖像數(shù)據(jù)中獲得了更具結(jié)構(gòu)化的多層特征表示，這種多層特征表示有效提高了SAR圖像目標(biāo)識別性能。為了獲得更高的訓(xùn)練效率和識別率，該文進(jìn)一步采用樸素貝葉斯準(zhǔn)則提出了一種對PGBN模型進(jìn)行分類的方法。實(shí)驗(yàn)采用MSTAR的3類目標(biāo)數(shù)據(jù)進(jìn)行了驗(yàn)證，結(jié)果表明通過該方法提取的特征有更好的結(jié)構(gòu)信息，對SAR圖像目標(biāo)識別具有較好的性能。
- SAR圖像 /
- 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

參考文獻(xiàn)(26)

張紅, 王超, 張波. 高分辨率SAR圖像目標(biāo)識別[M]. 北京: 科學(xué)出版社, 2009: 5.2 節(jié).

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

保錚, 邢孟道, 王彤. 雷達(dá)成像技術(shù)[M]. 北京: 電子工業(yè)出版社, 2004: 1.1 節(jié).

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, 楊汝良. 基于ICA和SVM的SAR圖像特征提取與目標(biāo)識別[J]. 計(jì)算機(jī)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.

龍泓琳, 皮亦鳴, 曹宗杰. 基于非負(fù)矩陣分解的SAR圖像目標(biāo)識別[J]. 電子學(xué)報(bào), 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.

孫洪. 高分辨率SAR圖像目標(biāo)識別[M]. 北京: 電子工業(yè)出版社, 2013: 5.1 節(jié).

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.

張學(xué)峰. 雷達(dá)高分辨距離像目標(biāo)識別與拒判方法研究[D]. [博士論文], 西安電子科技大學(xué), 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.

丁軍, 劉宏偉, 陳渤. 相似性約束的深度置信網(wǎng)絡(luò)在SAR圖像目標(biāo)識別中的應(yīng)用[J]. 電子與信息學(xué)報(bào), 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.

丁軍, 劉宏偉, 王英華, 等. 一種聯(lián)合陰影和目標(biāo)區(qū)域圖像的SAR目標(biāo)識別方法[J]. 電子與信息學(xué)報(bào), 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.

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪問統(tǒng)計(jì)