Utilizing semi-parametric model to compensate systematic errors in photogrammetry

Huiping Zhu; Li Yan; Fei Deng

doi:10.1117/12.833177

30 October 2009 Utilizing semi-parametric model to compensate systematic errors in photogrammetry

Huiping Zhu, Li Yan, Fei Deng

Proceedings Volume 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications; 74982L (2009) https://doi.org/10.1117/12.833177
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China

Abstract

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.

Citation Download Citation

Huiping Zhu, Li Yan, and Fei Deng "Utilizing semi-parametric model to compensate systematic errors in photogrammetry", Proc. SPIE 7498, MIPPR 2009: Remote Sensing and GIS Data Processing and Other Applications, 74982L (30 October 2009); https://doi.org/10.1117/12.833177

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