Learning optimal parameters for self-diagnosis in a system for automatic exterior orientation

Abstract

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. © Springer-Verlag Berlin Heidelberg 2003.