| A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection |
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| 引用本文： | 郎海涛,张杰,王一多,张晰,孟俊敏.A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection[J].海洋学报(英文版),2016,35(9):117-125. |
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| 作者姓名： | 郎海涛 张杰 王一多 张晰 孟俊敏 |
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| 作者单位： | 北京化工大学, 理学院物理电子系, 北京 100029, 中国;南京信息工程大学, 江苏省大数据分析技术重点实验室, 南京 210044, 中国,国家海洋局第一海洋研究所, 青岛 266071, 中国,北京化工大学, 理学院物理电子系, 北京 100029, 中国,国家海洋局第一海洋研究所, 青岛 266071, 中国,国家海洋局第一海洋研究所, 青岛 266071, 中国 |
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| 摘 要： | To dates,most ship detection approaches for single-pol synthetic aperture radar(SAR) imagery try to ensure a constant false-alarm rate(CFAR).A high performance ship detector relies on two key components:an accurate estimation to a sea surface distribution and a fine designed CFAR algorithm.First,a novel nonparametric sea surface distribution estimation method is developed based on n-order Bézier curve.To estimate the sea surface distribution using n-order Bézier curve,an explicit analytical solution is derived based on a least square optimization,and the optimal selection also is presented to two essential parameters,the order n of Bézier curve and the number m of sample points.Next,to validate the ship detection performance of the estimated sea surface distribution,the estimated sea surface distribution by n-order Bézier curve is combined with a cell averaging CFAR(CA-CFAR).To eliminate the possible interfering ship targets in background window,an improved automatic censoring method is applied.Comprehensive experiments prove that in terms of sea surface estimation performance,the proposed method is as good as a traditional nonparametric Parzen window kernel method,and in most cases,outperforms two widely used parametric methods,K and G0 models.In terms of computation speed,a major advantage of the proposed estimation method is the time consuming only depended on the number m of sample points while independent of imagery size,which makes it can achieve a significant speed improvement to the Parzen window kernel method,and in some cases,it is even faster than two parametric methods.In terms of ship detection performance,the experiments show that the ship detector which constructed by the proposed sea surface distribution model and the given CA-CFAR algorithm has wide adaptability to different SAR sensors,resolutions and sea surface homogeneities and obtains a leading performance on the test dataset.
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| 关 键 词： | Bé zier曲线 非参数方法 船只检测 海表面分布 合成孔径雷达 |
| 收稿时间： | 2015/8/31 0:00:00 |
| 修稿时间： | 2015/12/28 0:00:00 |
A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection |
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| LANG Haitao,ZHANG Jie,WANG Yiduo,ZHANG Xi and MENG Junmin.A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection[J].Acta Oceanologica Sinica,2016,35(9):117-125. |
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| Authors: | LANG Haitao ZHANG Jie WANG Yiduo ZHANG Xi and MENG Junmin |
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| Institution: | 1.School of Science, Beijing University of Chemical Technology, Beijing 100029, China;Jiangsu Key Laboratory of Big Data Analysis Technology/B-DAT, Nanjing University of Information Science & Technology, Nanjing 210044, China2.The First Institute of Oceanography, State Oceanic Administration, Qingdao 266061, China3.School of Science, Beijing University of Chemical Technology, Beijing 100029, China |
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| Abstract: | To dates, most ship detection approaches for single-pol synthetic aperture radar (SAR) imagery try to ensure a constant false-alarm rate (CFAR). A high performance ship detector relies on two key components:an accurate estimation to a sea surface distribution and a fine designed CFAR algorithm. First, a novel nonparametric sea surface distribution estimation method is developed based on n-order Bézier curve. To estimate the sea surface distribution using n-order Bézier curve, an explicit analytical solution is derived based on a least square optimization, and the optimal selection also is presented to two essential parameters, the order n of Bézier curve and the number m of sample points. Next, to validate the ship detection performance of the estimated sea surface distribution, the estimated sea surface distribution by n-order Bézier curve is combined with a cell averaging CFAR (CA-CFAR). To eliminate the possible interfering ship targets in background window, an improved automatic censoring method is applied. Comprehensive experiments prove that in terms of sea surface estimation performance, the proposed method is as good as a traditional nonparametric Parzen window kernel method, and in most cases, outperforms two widely used parametric methods, K and G0 models. In terms of computation speed, a major advantage of the proposed estimation method is the time consuming only depended on the number m of sample points while independent of imagery size, which makes it can achieve a significant speed improvement to the Parzen window kernel method, and in some cases, it is even faster than two parametric methods. In terms of ship detection performance, the experiments show that the ship detector which constructed by the proposed sea surface distribution model and the given CA-CFAR algorithm has wide adaptability to different SAR sensors, resolutions and sea surface homogeneities and obtains a leading performance on the test dataset. |
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| Keywords: | Bé zier curve nonparametric method ship detection sea surface distribution synthetic aperture radar |
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