高分辨SAR图像自动区域筛选目标检测算法

宋文青; 王英华; 刘宏伟

doi:10.11999/JEIT150808

高分辨SAR图像自动区域筛选目标检测算法

doi: 10.11999/JEIT150808

基金项目:

国家自然科学基金(61201292, 61322103, 61372132)，全国优秀博士学位论文作者专项资金(FANEDD-201156)，中央高校基本科研业务费专项资金

计量
- 文章访问数: 1574
- HTML全文浏览量: 113
- PDF下载量: 699
- 被引次数: 0
出版历程
- 收稿日期: 2015-07-08
- 修回日期: 2015-11-20
- 刊出日期: 2016-05-19

An Automatic Block-to-block Censoring Target Detector for High Resolution SAR Image

Funds:

The National Natural Science Foundation of China (61201292, 61322103, 61372132), The Foundation for the Author of National Excellent Doctoral Dissertation of China (FANEDD-201156), The Fundamental Research Funds for the Central Universities

摘要

摘要: 在G0分布背景杂波假设下，基于VI-CFAR算法该文提出一种自动区域筛选的恒虚警目标检测算法，以解决高分辨SAR图像复杂环境背景下的目标检测问题。该算法首先利用变化指数(VI)统计量对局部参考窗内的均匀区域进行筛选，以剔除参考窗内具有目标干扰点的非均匀区域；然后利用均值比(MR)统计量对参考窗内同质的均匀区域进行区域合并，以解决杂波边界处的背景杂波筛选问题；最后利用筛选到的同质均匀区域内的像素集合进行背景杂波参数估计，对待检测区域实现二值检测。通过实测SAR图像车辆目标检测实验表明，在多目标和杂波边界复杂环境背景下，该算法具有较稳定的检测性能和虚警抑制能力。
- 恒虚警检测 /
- 区域筛选 /
- 变化指数 /
- 均值比 /
- G0分布
Abstract: Assuming theG0 distribution clutter background, an automatic block-to-block censoring CFAR (ABC-CFAR) detector is proposed based on VI-CFAR for high resolution SAR image in nonhomogeneous environments. Firstly the Variability Index (VI) statistic is used to censor the blocks in the local reference window in order to reject the non-homogeneous ones in which there exists interfering target samples. Then the Mean Ratio (MR) statistic is utilized to select and combine the homogeneous blocks which have the same distribution, in order to solve background clutter censoring problem in clutter edge situation. At last, with the selected blocks, the distribution parameters of the background clutter are estimated, and then the binary detection is implemented in the Block Under Test (BUT). Using the real SAR image data including ground vehicle targets, the experimental results show that the proposed ABC-CFAR detector has robust detection performance and false alarm regulation property in multi-target and clutter edge nonhomogeneous environment.
- Block-to-block censoring /
- Variability Index (VI) /
- Mean Ratio (MR) /
- G0distribution /

HTML全文

参考文献(23)

何友, 黄勇, 关键, 等. 海杂波中的雷达目标检测技术综述[J]. 现代雷达,　2014, 36(12): 1-9.

HE You, HUANG Yong, GUAN Jian, et al. An overview on radar target detection in sea clutter[J]. Modern Radar,　2014, 36(12): 1-9.

张小强, 熊博莅, 匡纲要. 一种基于变化检测技术的 SAR 图像舰船目标鉴别方法[J]. 电子与信息学报,　2015 , 37(1): 63-70. doi: 10.11999/JEIT140143.

ZHANG Xiaoqiang, XIONG Boli, and KUANG Gangyao. A ship target discrimination method based on change detection in SAR imagery[J]. Journal of Electronics Information Technology, 2015, 37(1): 63-70. doi: 10.11999/JEIT140143.

WANG Yinghua and LIU Hongwei. PolSAR ship detection based on superpixel-level scattering mechanism distribution features[J]. IEEE Geoscience and Remote Sensing Letters,　2015, 12 (8): 1780-1784. doi: 10.1109/LGRS.2015.2425873.

El-Darymli K, McGuire P, Power D, et al. Target detection in synthetic aperture radar imagery: a state-of-the-art survey[J]. Journal of Applied Remote Sensing, 2013, 7(1): 1-35. doi: 10.1117/1.JRS.7.071598.

Mishne G, Talmon R, and Cohen I. Graph-based supervised automatic target detection[J]. IEEE Transactions on Geoscience and Remote Sensing, 　2015, 53 (5): 2738-2754. doi: 10.1109/TGRS.2014.2364333.

HOU Biao, CHENG Xingzhong, and JIAO Licheng. Multilayer CFAR detection of ship targets in very high resolution SAR images[J]. IEEE Geoscience and Remote Sensing Letters, 2015, 12 (4): 811-815.

ZHANG Yangrui, GAO Meiguo, and LI Yunjie. Performance analysis of typical mean-level CFAR detectors in the interfering target background[C]. IEEE 9th Conference on (Industrial Electronics and Applications) ICIEA, Hangzhou, 2014: 1045-1048.

Rickard J T and Dillard G M. Adaptive detection algorithms for multiple target situations[J]. IEEE Transactions on Aerospace and Electronic Systems, 1977, 13(4): 338-343. doi: 10.1109/TAES.1977.308466.

Ghandhi P P and Kassam S A. Analysis of CFAR processors in nonhomogeneous background[J]. IEEE Transactions on Aerospace and Electronic Systems, 1988, 24(4): 427-445.

Himonas S D and Barkat M. Automatic censored CFAR detection for non-homogeneous environments[J]. IEEE Transactions on Aerospace and Electronic Systems, 1992, 28(1): 286-304.

Smith M E and Varshney P K. Intelligent CFAR processor based on data variability[J]. IEEE Transactions on Aerospace and Electronic Systems, 2000, 36(3): 837-847. doi: 10.1109/ 7.869503.

Farrouki A and Barkat M. Automatic censoring CFAR detector based on ordered data variability for nonhomogeneous environments[J]. IEE Proceedings of Radar, Sonar and Navigation, 2005, 152(1): 43-51. doi: 10.1049/ ip-rsn: 20045006.

Zaimbashi A and Norouzi Y. Automatic dual censoring cell averaging CFAR detector in nonhomogeneous environments [J]. Signal Processing, 2008, 88(11): 2611-2621. doi: 10.1016/j. sigpro.2008.04.016.

Zaimbashi A. An adaptive cell averaging-based CFAR detector for interfering targets and clutter-edge situations[J]. Digital Signal Processing, 2014, 31: 59-68. doi: 10.1016/j. dsp.2014.04.005.

Almarshad M N, Barkat M, and Alshebeili S A. A monte carlo simulation for two novel automatic censoring techniques of radar interfering targets in log-normal clutter[J]. Signal Processing, 2008, 88(3): 719-732. doi: 10.1016/j.sigpro.2007. 09.013.

Chabbi S, Laroussi T, and Barkat M. Performance analysis of dual automatic censoring and detection in heterogeneous Weibull clutter: A comparison through extensive simulations [J]. Signal Processing, 2013, 99(11): 2879-2893. doi: j.sigpro. 2013.03.026.

Frery A C, Muller H J, Yanasse C C F, et al. A model for extremely heterogeneous clutter[J]. IEEE Transactions on Geoscience and Remote Sensing, 1997, 35(3): 648-659. doi: 10.1109/36.581981.

GAO Gui, LIU Li, ZHAO Lingjun, et al. An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(6): 1685-1697. doi: 10.1109/TGRS.2008.2006504.

YU Wenyi, WANG Yinghua, LIU Hongwei, et al. Superpixel- based CFAR target detection for high-resolution SAR images [J]. IEEE Geoscience and Remote Sensing Letters, 2016, pp(99): 1-5. doi: 10.1109/LGRS.2016.2540809.

Salazar J C. II. Detection schemes for synthetic aperture radar imagery based on a beta prime statistical model[D]. [Ph.D. dissertation], Florida University, 1999.

Kreithen D E, Halversen S D, and Owirka G J. Discriminating targets from clutter[J]. The Lincoln Laboratory Journal, 1993, 6(1): 25-51.

施引文献

资源附件(0)

访问统计