Among various methods to detect the ship targets in Synthetic Aperture Radar(SAR)image,the Constant False Alarm Rate(CFAR)detection algorithm with an adaptive detection threshold is the most important and extensively used one.In order to improve the detection performance for ships in SAR image,various statistical distributions are applied,with an attempt to accurately model the SAR clutter backgrounds,such as Gamma distribution,K distribution,log-normal distribution,G0 distribution and the alpha-stable distribution,etc.In modern radar systems,the use of the CFAR technique is necessary to keep the false alarms at a suitably low rate in an a priori unknown time-varying and spatially nonhomogeneous backgrounds,and to improve the detection probability as much as possible.The clutter background in SAR images is complicated and variable,when the actual clutter background deviates from the assumed statistical distribution,the performance of the parametric CFAR detectors deteriorates,whereas the nonparametric CFAR method exhibits its advantage.In this paper,the Wilcoxon nonparametric CFAR scheme for ship detection in SAR image is proposed and analyzed.By comparison with several typical parametric CFAR schemes on 3 real SAR images of Radarsat-2,ICEYE-X6 and Gaofen-3,the robustness of the Wilcoxon nonparametric detector to maintain a good false alarm performance in these different detection backgrounds is revealed,and its detection performance for the weak ship is improved evidently.Moreover,the detection speed of the Wilcoxon nonparametric detector is fast,and it has the simplicity of hardware implementation.
关键词
SAR图像/雷达杂波/目标检测/恒虚警率/非参数
Key words
SAR image/Radar clutter/Target detection/Constant False Alarm Rate(CFAR)/Nonparametric
维普
万方数据