A Novel Absolute Orientation Method Using Local Similarities Representation

Absolute orientation is an important method in the field of photogrammetry. The technique is used to transform points between a local coordinate reference system and a global (geodetic) reference system. The classical transformation method uses a single set of similarity transformation parameters. However, the root mean square error (RMSE) of the classical method is large, especially for large-scale aerial photogrammetry analyses in which the points used are triangulated through free-net bundle adjustment. To improve the transformation accuracy, this study proposes a novel absolute orientation method in which the transformation uses various sets of local similarities. A Triangular Irregular Network (TIN) model is applied to divide the Ground Control Points (GCPs) into numerous triangles. Local similarities can then be computed using the three vertices of each triangle. These local similarities are combined to formulate the new transformation based on a weighting function. Both simulated and real data sets were used to assess the accuracy of the proposed method. The proposed method yields significantly improved plane and z-direction transformed point accuracies compared with the classical method. On a real data set with a mapping scale of 1:30,000 for a 53 km x 35 km study area, the plane and z RMSEs can be reduced from 1.2 m and 12.4 m to 0.4 m and 3.2 m, respectively.