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

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.