Fast Model-based Automatic Target Recognition Method for Synthetic Aperture Sonar Image
Zhu Zhao-tong① Peng Shi-bao② Xu Jia③ Xu Xiao-mei①
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) ③(School of Information and Electronic, Beijing Institute of Technology, Beijing 100081, China)
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.
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.
[2]
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.
[3]
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.
[4]
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.
[5]
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.
[6]
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.
[7]
Dobeck G J and Hyland J C. Automated detection and classification of sea mines in sonar imagery[J]. SPIE, 1997, Vol. 3097: 90-110.
[8]
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.
[9]
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.
[10]
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.
[11]
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.
[12]
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.
[13]
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.
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.
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.
Ni Xin-qiang. Research of classification and automatic target recognition using SAR imagery[D]. [Ph.D. dissertation], University of Chinese Academy of Sciences, 2007.
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.
[18]
Katz S, Tal A, and Basri R. Direct visibility of point sets[J]. ACM Transactions on Graphics, 2007, 26(3): 1-24.
