Computational-vision based orthorectification and georefrencing for correct localization of railway track in UAV imagery

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.