SPACE INTERSECTION BY COLLINEARITY

Abstract

Intersection refers to the determination of a point’s position in object space by intersecting the image rays from two or more images. The standard method is application of the collinearity equations, with two equations for each image of the point. Approximate coordinates of the point, calculated by collinearity equations. EOPs are obtained by using space resection. Initial approximations are required for ground coordinate. In this experiment, we use several data types on flat, rugged, and incline terrain. The data has random and systematic error. We create a simulated data of ground coordinate points then we compute the image points using collinearity equations. Finally, we can conclude that the data with systematic error achieves the best precise than data with random error. This is occurred in all of terrain types. The RMS error in data with systematic error achieves constantly after the limitation number 100 control points. Therefore, data with systematic radial lens error can be used in intersection case. The RMSx is more than RMSy in almost all of data types.