摘要: This paper describes the algorithm used to further automate the workflow to triangulate images from the multi-line Aerial Digital Sensor ADS from Leica Geosystems. Up to now many parts of the triangulation process have been succes... 展开 This paper describes the algorithm used to further automate the workflow to triangulate images from the multi-line Aerial Digital Sensor ADS from Leica Geosystems. Up to now many parts of the triangulation process have been successfully automated like point measurement, bundle adjustment with automatic blunder removal, automatic variance components estimation and automatic selection of self-calibration parameters. What still requires human interaction is the quality analysis. The key to full automation in triangulation is the quality control. The new process is characterized by a loop, which consists of automatic point measurement, bundle adjustment and automatic quality control. The goal of the process is to obtain the orientation for the block automatically with minimum user interaction. The required quality of the project in terms of accuracy on the ground is specified by the user at project start. The process starts from a sparse tie point pattern, which is successively densified until the requested quality is achieved. Typical for triangulation of line sensors is the use of orientation fixes. In this new approach the time or distance between two orientation fixes is no longer constant. Instead orientation fixes are placed at variable intervals. Those intervals are automatically determined. For quality control each strip is divided into sections, which are defined by the region between two orientation fixes. Each section is further divided into cells. The analysis based on statistical criteria is applied to each cell. To trust in automatically generated results the reliability is important. Therefore the quality criteria are based on external reliability values. As the quality is based on regions on the ground it can be presented in a simple colour coded form. Green means that the requested quality was achieved and red means that this region requires further attention. This way the user is directly guided to those areas where the algorithm could not fulfill the requested criteria. The advantage of the new algorithm is that it works much faster compared to the approach with constant spacing of orientation fixes and very dense point pattern. The old approach was always aiming at best quality, which may not be needed in every project. The new approach will create orientation values with a quality, which is sufficient for the project. 收起