Research on shadow enhancement for synthetic aperture sonar images

被引:0
作者
Zhang, Peng-Fei [1 ]
Liu, Wei [1 ]
Jiang, Ze-Lin [1 ]
Liu, Ji-Yuan [1 ]
Zhang, Chun-Hua [1 ]
机构
[1] Institute of Acoustics, Chinese Academy of Sciences, Beijing
来源
Binggong Xuebao/Acta Armamentarii | 2015年 / 36卷 / 02期
关键词
Information processing; Non-uniform sampling; Shadow enhancement; Synthetic aperture sonar; Target recognition;
D O I
10.3969/j.issn.1000-1093.2015.02.017
中图分类号
学科分类号
摘要
Synthetic aperture sonar needs to collect the echoes from a target at different locations, which blurs the contour of shadow cast of targets. The conventional methods cannot obtain good effects because of lack in exploitation of synthetic aperture principle. A rigorous derivation of fixed focus shadow enhancement (FFSE) algorithm is presented, and time domain, frequency domain and range-Doppler domain are conducted. For synthetic aperture sonars, many receivers are used to increase efficiency. Non-uniform fast Fourier transform (NSFFT) is used to eliminate non-uniform sampling in azimuth direction, and then shadow enhancement can be applied in range-Doppler domain. The method is applied to lake-trial data set and sea-trial data set. Results show that the shadows of objects are enhanced evidently, which is great significance to image segmentation and target identification. ©, 2015, China Ordnance Society. All right reserved.
引用
收藏
页码:305 / 312
页数:7
相关论文
共 17 条
[1]  
Hayes M.P., Gough P.T., Synthetic aperture sonar: A review of current status, IEEE Journal of Oceanic Engineering, 34, 3, pp. 207-224, (2009)
[2]  
Hansen R.E., Callow H.J., Sabo T.O., Et al., Challenges in seafloor imaging and mapping with synthetic aperture sonar, IEEE Transactions on Geoscience and Remote Sensing, 49, 10, pp. 3677-3687, (2011)
[3]  
Hunter A.J., Vossen R.V., Quesson B.A., Et al., Low frequency synthetic aperture sonar for detecting and classifying buried objects, Proceedings of 11th European Conference on Underwater Acoustics, pp. 1-8, (2012)
[4]  
Bryner D., Srivastava A., Huynh Q., Elastic shapes models for improving segmentation of object boundaries in synthetic aperture sonar images, Computer Vision and Image Understanding, 117, 12, pp. 1695-1710, (2013)
[5]  
Myers V., Fawcett J., A template matching procedure for automatic target recognition in synthetic aperture sonar imagery, IEEE Signal Processing Letters, 17, 7, pp. 683-686, (2010)
[6]  
Myers V., Williams D.P., Adaptive multiview target classification in synthetic aperture sonar images using a partially observable Markov decision process, IEEE Journal of Oceanic Engineering, 37, 1, pp. 45-55, (2012)
[7]  
Ye X.-F., Wang X.-M., Fang C., Et al., Study of sonar imagery segmentation algorithm base on improved Markov random field model, Acta Armamentarii, 30, 8, pp. 1039-1045, (2009)
[8]  
Guo H.-T., Sun D.-J., Tian T., The bounded histogram and its application in the sonar image enhancement with fuzzy sets, Journal of Electronics & Information Technology, 24, 9, pp. 1287-1290, (2002)
[9]  
Li S.-Q., Teng H.-Z., Ling Y., Et al., Real-time enhancing technique for sea-bottom sonar image, Applied Acoustics, 25, 5, pp. 284-289, (2006)
[10]  
Zhang T.D., Wan L., Xu Y.R., Et al., Sonar image enhancement based on particle swarm optimization, 3rd IEEE Conference on Industrial Electronics and Applications, pp. 2216-2221, (2008)