Abstract:
In recent years, reliable rail track health monitoring and localization requires accurately orthorectified and georeferenced imagery. The vision-based approach is most suited for the geometrical correction of unmanned aerial vehicles (UAVs) high-resolution imagery in the given scenario. The single image acquired by UAV covers a significant area and contains only one reference point and many distorted pixels. This paper provides a novel computational vision-based approach for orthorectification and georeferencing of a single rail track aerial image among the set of given images without an exclusive reference map of that location and ground control points.