Undistorting the past: new techniques for orthorectification of archaeological aerial frame imagery

Archaeologists using airborne data can encounter a large variety of frame images in the course of their work. These range from vertical aerial photographs acquired with very expensive calibrated optics to oblique images from hand-held, uncalibrated cameras and even photographs shot with compact cameras from an array of unmanned airborne solutions. Additionally, imagery can be recorded in one or more spectral bands of the complete optical electromagnetic spectrum. However, these aerial images are rather useless from an archaeological standpoint as long as they are not interpreted in detail. Furthermore, the relevant archaeological information interpreted from these images has to be mapped and compared with information from other sources. To this end, the imagery must be accurately georeferenced, and the many geometrical distortions induced by the optics, the terrain and the camera tilt should be corrected. This chapter focuses on several types of archaeological airborne frame imagery, the distortion factors that are influencing these two-dimensional still images and the necessary steps to compute orthophotographs from them. Rather than detailing the conventional photogrammetric orthorectification workflows, this chapter mainly centres on the use of computer vision-based solutions such as structure from motion (SfM) and dense multi-view stereo (MVS). In addition to a theoretical underpinning of the working principles and algorithmic steps included in both SfM and MVS, real-world imagery originating from traditional and more advanced airborne imaging platforms will be used to illustrate the possibilities of such a computer vision-based approach: the variety of imagery that can be dealt with, how (accurately) these images can be transformed into map-like orthophotographs and how these results can aid in the documentation of archaeological resources at a variety of spatial scales. Moreover, the case studies detailed in this chapter will also prove that this approach might move beyond current restrictions of conventional photogrammetry due to its applicability to datasets that were previously thought to be unsuitable for convenient georeferencing