Ideally, a camera from a UAV acquires consecutive images focusing on a point in the terrain directly below it, following the plumb line (nadir point). The top of the objects in this point is visible and their shape is unchanged, but this is not the case when an image is not taken directly pointing down, terrain along the scene is not flat or for the object captured away from the center of the frame. In these cases, the sides of objects are seen instead of the top and the distortion becomes greater as the object is farther from the center. This is useful when generating a 3D model because we see all sides of the objects of interest, but when we want a 2D map there is one step more to do.
The objective of orthophoto generation is, therefore, to elminate any distortions owing to camera tilt and ground relief, thereby maintaining the scene/image’s actual dimensions. Orthophoto generation requires georeferenced photos and a Digital Surface Model (DSM). The accuracy of the orthophotos depends, to a very large extent, on the vertical accuracy of the DSM. This is particularly the case in rugged terrain with high terrain distortions.
Figure 5.13: UAV orthophoto mosaic of an agricultural area in Tanzania (source: STARS AgriSense team).
Figure 5.14: Orthophoto of a wheat field in Bangladesh (source: STARS CIMMYT team)
To generate this geometrically corrected image the processing software uses the position of the camera with respect to the terrain in each image captured (georeference), stretches (projects) the images as needed to correct the perspective distortions based on the DSM and selects the images that show better the top of the objects (centers of the images) to create a fusion of all the corrected images.
As part of the processing, reports on the geolocation and pixel reprojection accuracy are generated. This helps to understand the quality of the results. If the DSM presents inaccuracies in certain points, there will be artifacts present in the derived orthophoto. If they are detected, a solution is to go back to the point cloud and clean it or add more manual tie points to improve the model.
Generated orthophotos can be used as base maps on which accurate measurements can be made. It should serve as the input for any further processing, e.g. 3D modelling, crop spectral signature extraction, crop classification, field boundary delineation, etc.