基于模型相似度拟合的海杂波统计方法

赵荻; 孟俊敏; 张晰; 郎海涛

doi:10.3969/j.issn.0253-4193.2015.05.011

基于模型相似度拟合的海杂波统计方法

doi: 10.3969/j.issn.0253-4193.2015.05.011

1.
北京化工大学应用物理系, 北京 100029
2.
国家海洋局第一海洋研究所, 山东青岛 266061
3.
北京化工大学应用物理系, 北京 100029;海洋遥测工程技术研究中心, 山东青岛 266061

基金项目: 海洋公益性行业科研专项经费项目(200905029,201505002-1);中央高校基本科研业务费专项资金北京化工大学(JD1414)。

计量
- 文章访问数: 1189
- HTML全文浏览量: 20
- PDF下载量: 1154
- 被引次数: 0
出版历程
- 收稿日期: 2014-02-21
- 修回日期: 2014-06-16

Sea clutter statistics based on similarity fitting of classical models

1.
Department of Applied Physics, Beijing University of Chemical Technology, Beijing 100029, China
2.
The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China
3.
Department of Applied Physics, Beijing University of Chemical Technology, Beijing 100029, China;Engineering Research Center for Marine Telemetry, State Oceanic Administration & China National Space Administration, Qingdao 266061, China

摘要

摘要: 本文提出一种基于模型相似度拟合的海杂波统计方法。首先根据合成孔径雷达(SAR)图像计算瑞利分布、对数正态分布、韦布尔分布、K分布、G⁰分布5种经典的海杂波分布的概率密度函数,然后根据模型间的相似度准则拟合得到新的海杂波分布模型。文章利用四景不同类型的真实SAR数据对算法的拟合性能进行了评价,结果显示利用该算法得到的拟合模型与真实SAR数据的平均Kullback-Leibler距离仅为0.015 84,远优于其他分布模型。基于该拟合模型的恒虚警率舰船检测算法对四景SAR数据的平均检测精度达到95.75%,在控制虚警和漏检方面均优于采用其他模型的同类方法。
- 海杂波统计模型 /
- 恒虚警率 /
- 舰船检测 /
- 合成孔径雷达
Abstract: A sea clutter statistical method based on similarity fitting is proposed in this paper. We first estimate five classical probability density functions of the sea clutter distribution in synthetic aperture radar (SAR) imagery,which include Rayleigh distribution,lognormal distribution,Weibull distribution,K distribution and G⁰ distribution. Thereafter,we fit all these five models to a new sea clutter distribution by an optimization method based on a similarity criterion. In the experiment,we evaluate the fitting precision based on the Kullback-Leibler distance using four SAR images. The results show the K-L distance between the fitting model and observations is only 0.015 84 when using the proposed algorithm. The fitting model is concluded to be superior to five classical distribution models. When we conduct ship detection using the fitting model and CFAR algorithm,the mean detection accuracy can reach up to 95.75%.
- sea clutter statistical model /
- constant false alarm rate (CFAR) /
- ship detection,synthetic aperture radar (SAR) /

HTML全文

参考文献(18)

Brusch S,Lehner S,Fritz T,et al. Ship surveillance with TerraSAR-X[J]. IEEE Transactions on Geoscience and Remote Sensing,2011,49(3):1092-1103.

Crisp D J. The state-of-the-art in ship detection in synthetic aperture radar imagery[R]. Defence Science And Technology Organisation Salisbury (Australia) Info Sciences Lab,2004.

Oliver C,Quegan S. Understanding synthetic aperture radar images[M]. Sci Tech Publishing,2004.

Ward K D. Compound representation of high resolution sea clutter[J]. Electronics Letters,1981,17(16): 561-563.

Ji Y,Zhang J,Meng J,et al. A new CFAR ship target detection method in SAR imagery[J]. Acta Oceanologica Sinica,2010,29(1): 12-16.

Jakeman E,Pusey P N. A model for non-Rayleigh sea echo[J]. IEEE Transactions on,Antennas and Propagation,1976,24(6): 806-814.

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.

鲁统臻,张杰,纪永刚,等. 基于G⁰分布的高海况SAR船只目标检测方法[J]. 海洋科学进展,2011,29(2):186-195. Lu Tongzhen,Zhang Jie,Ji Yonggang,et al. Ship target detection algorithm based on G⁰ distribution for SAR images under rough sea conditions[J].Advances in Marine Science,2011,29(2):186-195.

Szajnowski W. Estimators of log-normal distribution parameters[J]. IEEE Trans action on Aerospace Electronic Systems,1977,13(5): 533-536.

Tsagaris V,Vozikis G. Ship detection modules based on ASAR and terassar data for Greek areas of interest[C]//IEEE 2nd International Conference on Space Technology (ICST),2011 :1-4.

李永晨,刘浏. SAR图像统计模型综述[J]. 计算机工程与应用,2013,49(13):180-186. Li Yongchen,Liu Liu. Review of statistical model of SAR image[J]. Computer Engineering and Applications,2013,49(13):180-186.

Gao G. Statistical modeling of SAR images: A survey[J]. Sensors,2010,10(1):775-795.

Qin X X,Zhou S L,Zou H X,et al. A CFAR detection algorithm for generalized gamma distributed background in high-resolution SAR images[J]. Geoscience and Remote Sensing Letters,IEEE,2013,10(4): 806-810.

Anastassopoulos V,Lampropoulos G A,Drosopoulos A,et al. High resolution radar clutter statistics[J].IEEE Transactions on Aerospace and Electronic Systems,1999,35(1):43-60.

陈祥,孙俊,尹奎英,等. 基于CFAR级联的SAR图像舰船目标检测算法[J]. 现代雷达,2012,34(9): 50-55. Chen Xiang,Sun Jun,Yin Kuiying,et al. An algorithm of ship target detection in SAR images based on cascaded CFAR[J]. Modern Radar,2012,34(9): 50-55.

Blake A P. High resolution SAR clutter textural analysis and simulation[J]. SPIE,1995,2584:101-108.

Li H C,Hong W,Wu Y R,et al. On the empirical-statistical modeling of SAR images with generalized gamma distribution[J]. Selected Topics in Signal Processing,IEEE,2011,5(3): 386-397.

Burnham K P,Anderson D R. Model selection and multi-model inference: a practical information-theoretic approach[M].New York: Springer,2002.

施引文献

资源附件(0)

访问统计