| LANG Haitao,ZHANG Jie,WANG Yiduo,ZHANG Xi,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 |
| 利用n阶Bézier曲线对合成孔径雷达图像海表面分布的估计及其在船只检测中的应用 |
| A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection |
| 投稿时间：2015-08-31 修订日期：2015-12-28 |
| DOI：10.1007/s13131-016-0924-8 |
| 中文关键词: Bézier曲线 非参数方法 船只检测 海表面分布 合成孔径雷达 |
| 英文关键词:Bézier curve nonparametric method ship detection sea surface distribution synthetic aperture radar |
| 基金项目: |
| 作者 | 单位 | E-mail | | 郎海涛 | 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 | langht@mail.buct.edu.cn | | 张杰 | 国家海洋局第一海洋研究所, 青岛 266071, 中国 | | | 王一多 | 北京化工大学, 理学院物理电子系, 北京 100029, 中国 | | | 张晰 | 国家海洋局第一海洋研究所, 青岛 266071, 中国 | | | 孟俊敏 | 国家海洋局第一海洋研究所, 青岛 266071, 中国 | |
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| 中文摘要: |
| 迄今为止，大多数单极化合成孔径雷达图像的船只检测都采用恒虚警率方法。检测器的性能取决于两个方面：精确的海表面分布估计和精心设计的恒虚警率检测算法。首先，发展了一种新的基于n阶Bézier曲线的非参数海表面分布估计方法，推导了基于最小平方优化的解析解，给出了两个核心参数Bézier曲线的阶数n和采样点数量m的优选结果。进而，为了评价基于所提出方法的船只检测性能，将估计的海表面分布模型与单元平均恒虚警率算法相结合构建了新的船只检测器。为了排除背景窗口中可能的干扰，采用了改进的自动筛选算法。实验结果表明：（1）在海表面拟合性能方面，所提出的方法与传统的Parzen窗核方法同样好，在多数情况下，优于两种广泛使用的参数模型：和模型；（2）在计算速度方面，所提出方法的主要优势在于时间消耗仅取决于采样点数量，而与图像大小无关，因此相比于Parzen窗核方法，计算速度得到极大的提升。在某些情况下，甚至比两种参数方法还要快。（3）在船只检测方面，所提出方法构建的检测器对不同的合成孔径雷达，不同分辨率，不同的海表面均匀度具有很好的适应性，在测试集中表现优异。 |
| 英文摘要: |
| 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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