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. doi: 10.1007/s13131-016-0924-8
Citation: 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. doi: 10.1007/s13131-016-0924-8

A synthetic aperture radar sea surface distribution estimation by n-order Bézier curve and its application in ship detection

doi: 10.1007/s13131-016-0924-8
  • Received Date: 2015-08-31
  • Rev Recd Date: 2015-12-28
  • 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.
  • loading
  • An Wentao,Xie Chunhua,Yuan Xinzhe.2014.An improved iterative censoring scheme for CFAR ship detection with SAR imagery.IEEE Transactions on Geoscience and Remote Sensing,52(8):4585-4595
    Bishop C M.1995.Neural Networks for Pattern Recognition.New York:Oxford University Press Brusch S,Lehner S,Fritz T,et al.2011.Ship surveillance with Ter-raSAR-X.IEEE Transactions on Geoscience and Remote Sensing, 49(3):1092-1103
    Crisp D J.2004.The state-of-the-art in ship detection in synthetic aperture radar imagery (No.DSTO-RR-0272).Salisbury:De-fence Science and Technology Organisation Salisbury (Australia)Info Sciences Lab
    Cui Yi,Zhou Guangyi,Yang Jian,et al.2011.On the iterative censoring for target detection in SAR images.IEEE Geoscience and Remote Sensing Letters,8(4):641-645
    El-Darymli K,McGuire P,Power D,et al.2013.Target detection in synthetic aperture radar imagery:a state-of-the-art survey.Bézier curves to improve the estimation precision;(2) Cui et al.Journal of Applied Remote Sensing,7(1):071598 LANG Haitao et al.Acta Oceanol.Sin.,2016,Vol.35,No.9,P.117-125
    Farouki R T.2012.The Bernstein polynomial basis:a centennial retrospective.Computer Aided Geometric Design,29(6):379-419
    Frery A C,Müller H J,Yanasse C D C F,et al.1997.A model for extremely heterogeneous clutter.IEEE Transactions on Geoscience and Remote Sensing,35(3):648-659
    Gao Gui.2010.Statistical modeling of SAR images:A survey.Sensors, 10(1):775-795
    Gao Gui.2011.A parzen-window-kernel-based CFAR algorithm for ship detection in SAR images.IEEE Geoscience and Remote Sensing Letters,8(3):557-561
    Gao Gui,Liu Li,Zhao Lingjun,et al.2009.An adaptive and fast CFAR algorithm based on automatic censoring for target detection in high-resolution SAR images.IEEE Transactions on Geoscience and Remote Sensing,47(6):1685-1697
    Ji Yonggang,Zhang Jie,Meng Junmin,et al.2010.A new CFAR ship target detection method in SAR imagery.Acta Oceanologica Sinica,29(1):12-16
    Krylov V A,Moser G,Serpico S B,et al.2011.Enhanced dictionary-based SAR amplitude distribution estimation and its validation with very high-resolution data.IEEE Geoscience and Remote Sensing Letters,8(1):148-152
    Krylov V A,Moser G,Serpico S B,et al.2013.On the method of logarithmic cumulants for parametric probability density function estimation.IEEE Transactions on Image Processing,22(10):3791-3806
    Kullback S.1987.The kullback-leibler distance.The American Statistician, 41(4):340-341
    Kuruo.lu E E,Zerubia J.2004.Modeling SAR images with a generalization of the Rayleigh distribution.IEEE Transactions on Image Processing,13(4):527-533
    Lang Haitao,Zhang Jie,Zhang Ting,et al.2014.Hierarchical ship detection and recognition with high-resolution polarimetric synthetic aperture radar imagery.Journal of Applied Remote Sensing, 8(1):083623
    Li Hengchao,Hong Wen,Wu Yirong,et al.2010.An efficient and flexible statistical model based on generalized Gamma distribution for amplitude SAR images.IEEE Transactions on Geoscience and Remote Sensing,48(6):2711-2722
    Li Hengchao,Hong Wen,Wu Yirong,et al.2011.On the empirical-statistical modeling of SAR images with generalized gamma distribution.IEEE Journal of Selected Topics in Signal Processing, 5(3):386-397
    Mantero P,Moser G,Serpico S B.2005.Partially supervised classification of remote sensing images through SVM-based probability density estimation.IEEE Transactions on Geoscience and Remote Sensing,43(3):559-570
    Moser G,Zerubia J,Serpico S B.2006a.Dictionary-based stochastic expectation-maximization for SAR amplitude probability density function estimation.IEEE Transactions on Geoscience and Remote Sensing,44(1):188-200
    Moser G,Zerubia J,Serpico S B.2006b.SAR amplitude probability density function estimation based on a generalized Gaussian model.IEEE Transactions on Image Processing,15(6):1429-1442
    Nicolas J M.2002.Introduction aux Statistiques de deuxième espèce:applications des Logs-moments et des Logs-cumulants àl'analyse des lois d'images radar.Traitement du Signal (in French), 19(3):139-167
    Oliver C J.1993.Optimum texture estimators for SAR clutter.Journal of Physics D:Applied Physics,26(11):1824-1835
    Oliver C,Quegan S.2004.Understanding Synthetic Aperture Radar Images.Raleigh,NC:SciTech Publishing Parzen E.1962.On estimation of a probability density function and mode.The Annals of Mathematical Statistics,33(3):1065-1076
    Stuart A,Keith J.2008.Kendall's Advanced Theory of Statistics.6th ed.New York:Wiley Wang Changcheng,Liao Mingsheng,Li Xiaofeng.2008.Ship detection in SAR image based on the alpha-stable distribution.Sensors, 8(8):4948-4960
    Zhao Di,Lang Haitao,Zhang Xi,et al.2015a.Sea clutter modeling by statistical majority consistency for ship detection in SAR imagery.IEEE International Geoscience and Remote Sensing Symposium (IGARSS).Milan:IEEE,3695-3698
    Zhao Di,Meng Junmin,Zhang Xi,et al.2015b.Sea clutter statistics based on similarity fitting of classical models.Haiyang Xuebao(in Chinese),37(5):112-120
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article views (945) PDF downloads(403) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return