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

Zhu Zhao-tong; Peng Shi-bao; Xu Jia; Xu Xiao-mei

doi:10.11999/JEIT141228

Volume 37 Issue 7

Jul. 2015

Turn off MathJax

Article Contents

Article Navigation > Journal of Electronics & Information Technology > 2015 > 37(7): 1757-1762

Zhu Zhao-tong, Peng Shi-bao, Xu Jia, Xu Xiao-mei. Fast Model-based Automatic Target Recognition Method for Synthetic Aperture Sonar Image[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1757-1762. doi: 10.11999/JEIT141228

Citation:

Zhu Zhao-tong, Peng Shi-bao, Xu Jia, Xu Xiao-mei. Fast Model-based Automatic Target Recognition Method for Synthetic Aperture Sonar Image[J]. Journal of Electronics & Information Technology, 2015, 37(7): 1757-1762. doi: 10.11999/JEIT141228

Citation:

PDF( 4974 KB)

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

doi: 10.11999/JEIT141228

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)

Received Date: 2014-09-23
Rev Recd Date: 2015-03-09
Publish Date: 2015-07-19

Abstract

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

FullText(HTML)

References(22)

References

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.

刘维, 张春华, 刘纪元. 合成孔径声呐三维数据仿真研究[J]. 系统仿真学报, 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建筑物快速图像仿真[J]. 电子与信息学报, 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.

倪心强. SAR图像分类与自动目标识别技术研究[D]. [博士论文], 中国科学院大学, 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.

程鹏飞, 闫浩文, 韩振辉. 一个求解多边形最小面积外接矩形的算法[J]. 工程图学学报, 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.

Relative Articles

Supplements(0)

Cited By

Proportional views