为了在复杂的地面场景中实现准确的自动目标检测，分析了地面场景图像经二维加窗的伪Wigner-Ville分布(PWVD)后，归一化的Rényi熵与其出现概率间存在的基于e指数的统计特性，以及人造目标的出现引起的地面场景中Rényi熵的统计特性变化，提出了一种新的基于Rényi熵的显著图生成和目标探测方法。对Renyi熵图像进行了均值滤波，然后滤波前后的图像相减得到熵残余图像，并经过高斯滤波获得显著图，最终通过简便的阈值分割，完成目标探测。实验结果表明，该方法对8幅不同场景图像中共计14个目标的探测概率为100%，虚警概率不大于7.1%。与传统方法相比，本文提出的方法能够更为有效地检测复杂地面背景中的**目标。

For improving the accuracy of automatic target detection in complex ground scenes,the statistical property between the Rényi entropy and its occurrence probability of images calculated through the Pseudo Wigner-Ville Distribution (PWVD) with a two dimension window was analyzed based on exponential function.Then the change of the statistical property caused by the appearing of man-made targets is investigated.The methods of saliency map generation and target detection based on Rényi entropy are proposed.Firstly, the image of Rényi entropy is smoothed by the average filter.Then, the image of residual Rényi entropy is obtained by the subtraction of fore-and-aft filter images, and the saliency map is obtained by Gaussian filter.Finally,the target detection is completed by segmenting the saliency map with a simple and convenient threshold method.Experimental results demonstrate that the detection probability and false alarm probability of the method are 100% and less than 7.1% respectively for 14 targets in 8 images.In comparison with traditional methods, the proposed method can detect the military targets from complex ground scenes effectively.