Linear Features in Photogrammetric Activities

Photogrammetric manipulation of imagery has been, for the major part, a point-based operation. The utilization of points remains to be convenient since few manually digitized points can be accurately obtained to carry out various photogrammetric orientation procedures (e.g., relative and absolute orientation as well as photogrammetric triangulation). On the other hand, the constant evolution of digital photogrammetry calls for the use of other primitives since distinct points fall short when attempting to derive higher level/semantic information from the input imagery. As a result, there has been a tremendous interest by photogrammetric researchers in utilizing linear features in various photogrammetric activities. This interest is attributed to the fact that the extraction of linear features from the image space is easier to automate than distinct points. On the other hand, object space linear features can be directly derived form terrestrial Mobile Mapping Systems (MMS), GIS databases, and/or existing maps. Moreover, automatic matching of linear features, either within overlapping images or between image and object space, is easier than that of distinct points. Finally, linear features possess more semantic information than distinct points since they likely correspond to object boundaries. Such semantics can be automatically identified in the input imagery to facilitate higher-level tasks (e.g., surface reconstruction and object recognition). This paper summarizes the authors’ prior research using linear features, which might be represented by analytical functions (e.g., straight-line segments) or irregular (free-form) shapes, in photogrammetric activities such as automatic space resection, photogrammetric triangulation, camera calibration, image matching, surface reconstruction, image registration, and absolute orientation as well as in medical applications. Current progress, future expectations, and possible research directions are discussed as well.