摘要
传统的Parzen窗检测算法假设目标占整个背景中较小的一部分,将SAR图像中的所有像素用于估计杂波概率密度函数,容易造成检测阈值的增大从而对不太明显的SAR图像舰船目标产生漏检。对此,提出了一种改进的Parzen窗检测算法,该算法通过自适应地设置目标窗口,将潜在的目标从检测图像中剔除,对剔除后的杂波背景采用Parzen窗进行非参数化的杂波模型估计,进而确定检测阈值,完成目标的检测。相比传统的Parzen窗检测算法,提出的SAR图像舰船目标检测算法减少了漏检数量,改善了检测性能。实测SAR图像的检测结果表明了该方法的有效性。
The classical Parzen algorithmis is based on the assumption that the targets occupy a small part of the SAR image and uses all pixels of the SAR image to estimate the probability density function of the clutter background. This method results in the elevation of the detection threshold, and then it is possible to miss targets that are less obvious. In order to overcome this problem,we proposed an improved Parzen detection algorithm. The proposed algorithm adaptive- ly sets the target windows according to the size of the target and deletes the potential targets from the background. Then it estimates the clutter distribution based on Parzen window method. Finally,it determines the detection threshold for the target detection. Compared with the traditional Parzen detection method, the proposed algorithm decreases the number of missing target and also improves the detection performance. The detection results with the real SAR images verify the effectiveness of the algorithm.
出处
《计算机科学》
CSCD
北大核心
2015年第B11期151-154,共4页
Computer Science
