Multi-Observed Block Adjustment for Satellite ImagesWithout Ground Control Points
Yao Xinghui1，2，3，You Hongjian1，2，3 ，Wang Feng1，2，3，Wu Dongxia4，Lu Xiaojun5
(1.University of Chinese Academy of Sciences，Beijing 100190，China；
2.Insititute of Electronics Chinese Academy of Sciences，Beijing 100190，China；
3.Key Laboratory of Technology in Geo\|spatial Information Processing and Application SystemInstitute of Electronics，Chinese Academy of Sciences，Beijing 100190，China；
4.Beijing Remote Sensing Information Institude，Beijing 100011，China；
5.China International Engineering Consulting Corporation，Beijing，100048，China)
Abstract：In order to ensure the accuracy of target area’s ground information，it is necessary to do geometric correction for remote sensing images.Geometric correction has a great influence on the application of remote sensing images.Traditional geometric correction needs ground control points.However，it is difficult to obtain ground control points in some places，such as abroad，western China and desert.To improve positioning accuracy without ground control points，multi\|overlapping block adjustment model is built.Different from traditional method，error equations are built depending on multi\|projection in image space of a single object.In this way，error equations can converge to more accurate solutions.By adjusting the rational polynomial coefficients of each image，the positioning errors in different directions are compensated to a certain extent.Thus the positioning accuracy of remote sensing images is improved.First，block adjustment model and error compensation model are built with RPC coefficients.Then，conjugate gradient algorithm is used to solve the error equations iteratively.Finally the RPC coefficients are adjusted to improve the accuracy of positioning without ground control points.The ZY\|3 data test shows that multi\|overlapping block adjustment model increase the plane positioning accuracy of remote sensing images from 19.8m to 12.9m and the method can effectively improve the absolute positioning accuracy of remote sensing images.