Single-image-based georeferencing for unmanned aerial vehicles

Georeferencing is important for many applications in precision farming, in particular those based on unmanned aerial vehicles (UAVs). In this context, georeferencing typically relates the optical features of UAV images to their actual position in the 3D world, creating a grid map of the area of interest. Although state-of-the-art georeferencing methods are very accurate, these methods rely on multiple-view geometry reconstruction, which requires largely overlapping images of high quality. Acquiring such images can be difficult in practice, given the low-cost requirements for precision farming. In this paper, we study the practical applications and challenges of a simple, computationally inexpensive and fast method for georeferencing that is solely based on single images. Our method only uses an affine transformation where the UAV’s height is adjusted by a digital terrain model and does not require overlapping images. We find that our single-image-based method can be used for smart farming applications, where spatial accuracies of around 25 cm are sufficient.