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

作　　者：王密[1] 常学立 朱映[1] 应荷香 成世文 

机构地区：[1]武汉大学测绘遥感信息工程国家重点实验室,湖北武汉430079 [2]浙江省地理信息中心,浙江杭州310012

出　　处：《武汉大学学报（信息科学版）》2014年第12期1395-1400,共6页Geomatics and Information Science of Wuhan University

基　　金：国家973计划资助项目(2014CB744201;2012CB719902);国家863计划资助项目(2011AA120203);新世纪优秀人才支持计划资助项目;全国博士学位论文作者专项资金资助项目(201249);长江学者和创新团队发展计划资助项目(IRT1278);国家自然科学基金资助项目(41371430)~~

摘　　要：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.

关 键 词：光学遥感 自动几何精校正 RANSAC 数据探测法 抗差估计 

分 类 号：P237[天文地球—摄影测量与遥感]