EVALUATING THE ACCURACY OF CLUSTERING TECHNIQUES FOR LOCATING GROUND CONTROL POINTS IN UAV PHOTOGRAMMETRY PROJECTS: SOME PRELIMINARY RESUALTS

Typically, in order to achieve the accuracy of a UAV photogrammetric project, it is necessary to measure a large number of ground control points. Capturing this number of points is a time-consuming and costly process, especially in mountainous and hilly areas. To reduce the number of control points, there exist several techniques that suggest optimal locations of the ground control points. Yet, due to the complexity of ground, most of these techniques can lead to either redundant or missing locations required for a project to be oriented properly. Therefore, in practice, usually, the location of the points is determined either by visiting the ground in person or by visiting the area of interest on Google Earth. Aiming to automate the process, we decided to is to examine the ability of clustering methods in selecting an optimal, and perhaps minimum, set of ground control points. In this study, we used ۳D distance as the clustering parameter. Taking determined cluster centres as optimal locations can provide even distribution for the location ground control points. For this, the Partitional, Hierarchical, Model, Fuzzy, Graph, and Modern clustering techniques were evaluated. The results showed that the Average Linkage, SOM and FCM are the best, from the consistency and accuracy points of views. It was also noted that the points selected using these three techniques, in addition to being less, provide models more accurate than those obtained using ground control points that are selected manually by the operator.

Keywords:

UAV , Clustering , Ground Control Points , Evaluation