遥感图像几何精纠正中更为有效的稳健估计方法

More Effective Robust Estimation Methods in the Geometric Exact Rectification of Remote Sensing Image

针对遥感图像的几何精度纠正,通过仿真实验,以含有不同观测值数量和粗差数量及不同粗差大小的几何精纠正为例,比较了13种常用稳健估计方法消除或减弱粗差的能力。仿真实验说明L1法和German-McClure法是遥感图像几何精纠正中相对更为有效的稳健估计方法。

Geometric exact rectification is one of the most important steps for remote sensing image processing, and the most useful math way is least square method. Under the conditions of gross error,which is unavoidable in the observed values, the results by using least square method are largely affected, but robust estimation method can effectively eliminate or weaken the influ- ences of outliers. However, different robust estimation methods have different abilities to elimi- nate or weaken the influences of outliers. Through simulation experiments, which were based on the illustrations of geometric exact rectification including different numbers of observations, dif- ferent numbers of gross errors and different values of gross errors as examples, the abilities of 13 commonly used robust estimation methods to eliminate or weaken the influences of outliers were compared. The simulation experiment shows that L1 method and German-McClure method were more effective robust estimation methods in the geometric fine rectification of remote sensing image.