期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2002
卷号：XXXIV Part 3 A
页码：150-155
出版社：Copernicus Publications
摘要：Automatic Single Photo Resection (SP R) remains to be one of the challenging problems in digital photogrammetry. Visibility and uniqueness of distinct control points in the input imagery limit robust automation of the pose estimation procedure. Recent advances in digital photogrammetry mandate adopting higher-level primitives such as free-form control linear features for replacing traditional control points. Linear features can be automatically extracted from the image space. On the other hand, object space control linear features can be obtained from an existing GIS layer containing 3-D vector data such as road network, or from terrestrial Mobile Mapping Systems (MMS). In this paper, we present a new approach for simultaneously determining the position and attitude of the involved imagery as well as the correspondence between image and object space features. This approach does not necessitate having one to one correspondences between image and object space primitives, which makes it robust against changes and/or discrepancies between them. This characteristic will be helpful in detecting changes between object and image space linear features (e.g. due to temporal effects). The parameter estimation and matching follow an optimal sequential procedure that depends on the magnitude and direction of image space displacements resulting from incremental changes to the Exterior Orientation Parameters (EOP ). Experimental results using real data proved the feasibility and robustness of our approach, especially when compared to those obtained through traditional manual procedures. Changes and/or discrepancies between the data sets are detected and highlighted through consistency analysis of the resulting correspondences
关键词：Data Fusion; Linear Features; Single Photo Resection; Matching; Robust Parameter Estimation and Change ; Detection
