SAR图像强回波目标检测的偏态分布方差方法

对复杂背景下的SAR图像强回波目标检测问题进行了研究，提出了基于偏态分布方差的自适应检测算法。首先设计一种新的基于偏态分布模型的方差滤波器，偏态分布方差滤波器可减少相干斑噪声对检测的不良影响，提高图像方差的差异性，即强目标回波边缘灰度方差相对于强目标回波灰度方差、背景杂波灰度方差更小。其次，算法改进了根据图像复杂度自动选取阈值方法，通过自适应检测小方差像素，实现强回波目标检测。仿真结果说明该算法能够对SAR图像强回波目标较快地进行准确检测。相比方差特征法(VAR)和扩展分形特征法(EF)在检测速度与虚警率方面均具有较大的优越性。

Partial Distribution Variance Algorithm for SAR Image Strong Echo Target Detection

 Target detection problem of strong echoes of SAR image in the complex background is studied and an adaptive target detection algorithm is presented based on partial distribution variance. First, a novel variance filter based on the partial distribution model is designed. The partial distribution variance filter can reduce negative impacts on detection and improve the diversity of image variance, i.e., the variance of grayscale at the edge of target region is smaller than those in the target region and in the background. Second, the algorithm modifies the method of auto-selecting threshold based on the image complexity to detect strong echoes by detecting the small variance pixels. The simulation results show that the proposed algorithm can detect the strong echoes in SAR image more quickly and exactly, and it has a great advantage over the VAR algorithm and the EF algorithm in the aspects of efficiency and false alarm probability.