Utilizing semi-parametric model to compensate systematic errors in photogrammetry

In photogrammetry data processing, the uncertainties in the observations will lead to model error, which is the difference between the model and the reality. This model error may cause wrong results if the traditional parametric model is used. In order to solve this problem, Semi-parametric model, based on parametric model, is implemented in this article. Semiparametric model introduces a non-parametric component to describe the uncertainties in the observation data and their influences. Both parametric and non-parametric unknowns are solved by penalized least squares. Testing results indicate, that in the existence of observation uncertainties, Semi-parametric model can effectively isolate model error, thereby making it a better approach than parametric model.