基于全局自适应提取的目标RCS成像测量算法

Research on RCS Measurement Methods Based on Global Adaptive Extraction

针对传统目标RCS成像测量中人工加窗提取算法在提取SAR图像中目标散射像时,窗函数类型与大小影响测量精度的问题,本文提出了一种基于全局自适应提取的目标RCS成像测量算法。该算法首先利用成像算法获得目标的高分辨SAR图像,接着基于最大类间方差准则选取自适应阈值,然后利用全局自适应阈值分割图像,并采用八连通准则提取目标散射图像,最后通过RCS反演获得目标RCS值。与传统方法相比,本文方法能自适应提取目标散射图像,测量结果更稳定、测量精度更高。

In response to the issue of measurement accuracy in extracting target scattering images from SAR images using the conventional manual windowing extraction algorithm for target RCS imaging measurement,which is affected by the type and size of the window function,a target RCS imaging measurement algorithm based on global adaptive extrac⁃tion is proposed.The algorithm initially utilizes imaging algorithms to acquire a high⁃resolution SAR image of the target.Subsequently,it employs the maximum inter⁃class variance criterion to select an adaptive threshold,and then utilizes a global adaptive threshold to segment the image,employs an eight⁃connectivity criterion to extract the target scattering images,and finally obtains the target RCS values through RCS inversion.Compared with the traditional methods,this approach adaptively extracts target scattering images,resulting in increased stability and higher measurement accuracy.