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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.