Synergistic Fusion of GPS and Photogrammetrically Generated Elevation Models

Abstract

Digital elevation models (DEMs) produced from photogrammetric data sources have long relied on the use of ground control points to give them scale and orientation. However, in areas such as coastlines, landslides, or glaciers, where identification of suitable natural features and pre-marking is difficult, the use of conventional ground control may be unfeasible. This paper reports on research that uses independently collected DEMs derived from kinematic GPS to orient surfaces produced by aerial photogrammetric methods, using a least-squares surface matching algorithm. During algorithm development, three stages of testing were carried out, using increasingly more complex datasets. Initially, simulated surfaces were used to validate the matching theory and program. Then, a DEM derived from conventional aerial photography was matched with a GPS model, highlighting the effectiveness of surface matching to recover systematic errors in datasets. Finally, surfaces derived from small format digital imagery were successfully fused with wireframe GPS surfaces, the high redundancy and automation potential creating an elegant and cheaper alternative to photocontrol.