提出了一种改进的CFAR船只探测算法。该方法采用PNN模型来估计海面雷达后向散射的概率分布模型,利用CFAR技术来确定整体阈值,采用基于交叉验证技术的黄金分割搜索法估算高斯分布的形状参数,使用区域生长法去除虚警。使用Radarsat图像对该方法进行了检验,并与改进前的算法进行了比较,结果显示该文的探测算法在探测精度和探测速度上均明显优于改进前的算法。

In this paper,we present an improved constant false alarm rate(CFAR)algorithm for ship detection in syn-thetic aperture radar(SAR)imagery.The algorithm includes the probabilistic neural networks(PNN),CFAR tech-nique,golden section method and area growth method.The PNN is used to estimate the probabilistic density function of radar backscatter from sea surface.The CFAR technique is applied to determine a threshold that differs ships form sea surface.The golden section method is used to estimate the shape parameter of the Gauss function while the area growth method is employed to remove the false alarm.The algorithm is applied to detect ships in Radarsat imagery.The compar-ison of the performance between the improved algorithm and the original algorithm is made.The results show that the im-proved CFAR algorithm performed better than the original one.