一種基于模型的合成孔徑聲吶圖像目標(biāo)快速識(shí)別方法

朱兆彤; 彭石寶; 許稼; 許肖梅

doi:10.11999/JEIT141228

一種基于模型的合成孔徑聲吶圖像目標(biāo)快速識(shí)別方法

doi: 10.11999/JEIT141228 cstr: 32379.14.JEIT141228

1.
(廈門大學(xué)水聲通信與海洋信息技術(shù)教育部重點(diǎn)實(shí)驗(yàn)室廈門 361005) ②(清華大學(xué)電子工程系北京 100084) ③(北京理工大學(xué)信息與電子學(xué)院北京 100081)

基金項(xiàng)目:

國家自然科學(xué)基金(61271391, 41176032, 41376040)

計(jì)量
- 文章訪問數(shù): 1369
- HTML全文瀏覽量: 131
- PDF下載量: 637
- 被引次數(shù): 0
出版歷程
- 收稿日期: 2014-09-23
- 修回日期: 2015-03-09
- 刊出日期: 2015-07-19

Fast Model-based Automatic Target Recognition Method for Synthetic Aperture Sonar Image

1.
(Key Laboratory of Underwater Acoustic Communication and Marine Information Technology (Xiamen University),Ministry of Education, Xiamen 361005, China)
2.
(Department of Electronic Engineering, Tsinghua University, Beijing 100084, China)

摘要

摘要: 針對基于合成孔徑聲吶(SAS)圖像目標(biāo)識(shí)別的先驗(yàn)?zāi)０瀚@取困難、運(yùn)算復(fù)雜度高的問題，該文提出一種基于模型的改進(jìn)型相關(guān)快速識(shí)別方法。首先，基于構(gòu)造凸殼估計(jì)目標(biāo)姿態(tài)角，實(shí)現(xiàn)目標(biāo)成像幾何關(guān)系的估計(jì)；其次，提出改進(jìn)的基于隱藏點(diǎn)移除的目標(biāo)圖像快速生成方法，可實(shí)時(shí)得到各備選目標(biāo)對應(yīng)成像幾何關(guān)系的仿真圖像；進(jìn)而基于圖像相關(guān)實(shí)現(xiàn)目標(biāo)圖像識(shí)別；最后，仿真實(shí)驗(yàn)證明了算法的有效性。仿真實(shí)驗(yàn)結(jié)果表明，相比于常規(guī)的直接模板識(shí)別方法，該方法識(shí)別率高，計(jì)算速度快。
- 合成孔徑聲吶 /
- 目標(biāo)自動(dòng)識(shí)別 /
- 模型識(shí)別 /
- 姿態(tài)角估計(jì) /
- 仿真模板
Abstract: A modified model-based method is proposed to obtain sufficient prior templates and reduce the computational complexity on Synthetic Aperture Sonar (SAS) automatic target recognition. First, a quick method based on build convex hull is proposed to estimate the target pose quickly as well as the SAS imaging geometry for the specified target. Second, an improved method based on Hidden Point Removal (HPR) algorithm is proposed to obtain the target SAS simulation image effectively. Accordingly, the target recognition is realized by the correlation between the test image and the simulated image. Finally, the effectiveness of the proposed method is verified by the simulation experiment. It is shown that the proposed method can achieve higher computational efficiency than the conventional direct templet-based method, but remain the same high recognition rate.
- Synthetic Aperture Sonar (SAS) /
- Automatic Target Recognition (ATR) /
- Model-based recognition /
- Pose estimation /
- Simulation template

HTML全文

參考文獻(xiàn)(22)

Williams D P and Fakiris E. Exploiting environmental information for improved underwater target classification in sonar imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(10): 6284-6297.

Tai F, Kraus D, and Zoubir A M. Contributions to automatic target recognition systems for underwater mine classification [J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(1): 505-518.

Myers V and Fawcett J. A template matching procedure for automatic target recognition in synthetic aperture sonar imagery[J]. IEEE Signal Processing Letters, 2010, 17(7): 683-686.

Myers V and Williams D P. Adaptive multiview target classification in synthetic aperture sonar images using a partially observable markov decision process[J]. IEEE Journal of Oceanic Engineering, 2012, 37(1): 45-55.

Groen J, Coiras E, Del Rio Vera J, et al.. Model-based sea mine classification with synthetic aperture sonar[J]. IET Radar, Sonar Navigation, 2010, 4(1): 62-73.

Williams D P. Bayesian data fusion of multiview synthetic aperture sonar imagery for seabed classification[J]. IEEE Transactions on Image Processing, 2009, 18(6): 1239-1254.

Dobeck G J and Hyland J C. Automated detection and classification of sea mines in sonar imagery[J]. SPIE, 1997, Vol. 3097: 90-110.

Zhang H, Nasrabadi N M, Zhang Y, et al.. Multi-view automatic target recognition using joint sparse representation[J]. IEEE Transactions on Aerospace and Electronic Systems, 2012, 48(3): 2481-2497.

Sun Y, Liu Z, Todorovic S, et al.. Adaptive boosting for SAR automatic target recognition[J]. IEEE Transactions on Aerospace and Electronic Systems, 2007, 43(1): 112-125.

Xing X, Ji K, Zou H, et al.. Sparse representation based SAR vehicle recognition along with aspect angle[J]. The Scientific World Journal, 2014, 2014(1): 1-10.

Dong G, Wang N, and Kuang G. Sparse representation of monogenic signal: with application to rarget recognition in SAR images[J]. IEEE Signal Processing Letters, 2014, 21(8): 952-956.

Huang Y, Peia J, Yanga J, et al.. Neighborhood geometric center scaling embedding for SAR ATR[J]. IEEE Transactions on Aerospace and Electronic Systems, 2014, 50(1): 180-192.

Zhu Z, Peng S, Xu J, et al.. A fast SAS image simulator based on HPR algorithm[C]. Oceans - San Diego, 2013, San Diego, CA, USA, 2013: 1-5.

劉維, 張春華, 劉紀(jì)元. 合成孔徑聲吶三維數(shù)據(jù)仿真研究[J]. 系統(tǒng)仿真學(xué)報(bào), 2008, 20(14): 3838-3841.

Liu Wei, Zhang Chun-hua, and Liu Ji-yuan. Research on synthetic aperture sonar 3-D data simulation[J]. Journal of System Simulation, 2008, 20(14): 3838-3841.

熊文昌, 王宏琦, 唐侃. 基于面元投影模型的SAR建筑物快速圖像仿真[J]. 電子與信息學(xué)報(bào), 2014, 36(5): 1062-1068.

Xiong Wen-chang, Wang Hong-qi, and Tang Kan. Fast SAR imaging simulation for urban structures based on facet projection model[J]. Journal of Electronics Information Technology, 2014, 36(5): 1062-1068.

倪心強(qiáng). SAR圖像分類與自動(dòng)目標(biāo)識(shí)別技術(shù)研究[D]. [博士論文], 中國科學(xué)院大學(xué), 2007.

Ni Xin-qiang. Research of classification and automatic target recognition using SAR imagery[D]. [Ph.D. dissertation], University of Chinese Academy of Sciences, 2007.

程鵬飛, 閆浩文, 韓振輝. 一個(gè)求解多邊形最小面積外接矩形的算法[J]. 工程圖學(xué)學(xué)報(bào), 2008, 29(1): 122-126.

Cheng Peng-fei, Yan Hao-wen, and Han Zhen-hui. An algorithm for computing the minimum area bounding rectangle of an arbitrary polygon[J]. Journal of Engineering Graphics, 2008, 29(1): 122-126.

Katz S, Tal A, and Basri R. Direct visibility of point sets[J]. ACM Transactions on Graphics, 2007, 26(3): 1-24.

相關(guān)文章

施引文獻(xiàn)

資源附件(0)

訪問統(tǒng)計(jì)