光学影像自动几何精校正中控制点粗差检测方法比较

Comparison of Three Gross Error Detection Methods in Automatic Geometric Correction for Optical Satellite Images

控制点粗差检测是保证光学影像自动几何精校正精度的重要环节。将数据探测法、抗差估计法和随机抽样一致性法(RANSAC)三种经典的粗差检测方法应用于光学影像自动几何精校正的控制点粗差检测中,详细阐述了三种方法检测控制点粗差的方法和流程,并在控制点粗差率为10%、20%、30%和60%的情况下,利用实际光学卫星影像分别对三种方法展开控制点粗差检测实验。实验结果表明RANSAC相比数据探测法和抗差估计法对粗差率的敏感性最小,具有更强的鲁棒性,更加适用于光学影像几何自动精校正中控制点的粗差检测。

Control point gross error detection is a critical step that guarantees the geometric correction accuracy of optical satellite images during automatic geometric correction.This paper focuses on comparison and analysis of the three classical gross error detection methods;data snooping,robust estimation（iteration method with variable weights）and random sample consensus（RANSAC）.First,the steps of the three methods are described in detail.Next,gross error detection experiments using the three methods conducted with different gross error rates,i.e.10%,20%,30% and 60%,respectively are reported.These experimental results show that RANSAC is more robust and less sensitive to the gross error rate than data snooping and robust estimation and therefore the most appropriate method for gross error detection in automatic geometric correction.