Learning Optimal Parameters for Self-Diagnosis in a System for Automatic Exterior Orientation

The paper describes the automatic learning of parameters for self-diagnosis of a system for automatic orientation of single aerial images used by the State Survey Department of Northrhine-Westfalia. The orientation is based on 3D lines as ground control features, and uses a sequence of probabilistic clustering, search and ML-estimation for robustly estimating the 6 parameters of the exterior orientation of an aerial image. The system is interpreted as a classifier, making an internal evaluation of its success. The classification is based on a number of parameters possibly relevant for self-diagnosis. A hand designed classifier reached 11 % false negatives and 2 % false positives on appr. 17 000 images. A first version of a new classifier using support vector machines is evaluated. Based on appr. 650 images the classifier reaches 2 % false negatives and 4 % false positives, indicating an increase in performance.

Authors and Affiliations

1. Institute for Photogrammetry, University of Bonn, Nussallee 15, 53115, Bonn, Germany

   Wolfgang Förstner & Thomas Läbe

Editors and Affiliations

1. INRIA Rhône-Alpes, 655 Ave de l’Europe, 38330, Montbonnot, France

   James L. Crowley

2. Montefiore Institute, University of Liège, 4000, Liège Sart-Tilman, Belgium

   Justus H. Piater

3. Automation and Control Institute, Vienna University of Technology, Gusshausstraße 27/376, 1040, Vienna, Austria

   Markus Vincze

4. Institute of Digital Image Processing, Joanneum Research, Wastiangasse 6, 8010, Graz, Austria

   Lucas Paletta

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