A Novel Method for Fast Positioning of Non-Standardized Ground Control Points in Drone Images

Abstract

Positioning the pixels of ground control points (GCPs) in drone images is an issue of great concern in the field of drone photogrammetry. The current mainstream automatic approaches are based on standardized markers, such as circular coded targets and point coded targets. There is no denying that introducing standardized markers improves the efficiency of positioning GCP pixels. However, the low flexibility leads to some drawbacks, such as the heavy logistical input in placing and maintaining GCP markers. Especially as drone photogrammetry steps into the era of large scenes, the logistical input in maintaining GCP markers becomes much more costly. This paper proposes a novel positioning method applicable for non-standardized GCPs. Firstly, regions of interest (ROIs) are extracted from drone images with stereovision technologies. Secondly, the quality of ROIs is evaluated using image entropy, and then the outliers are filtered by an adjusted boxplot. Thirdly, pixels of interest are searched with a corner detector, and the precise imagery coordinates are obtained by subpixel optimization. Finally, the verification was carried out in an urban scene, and the results show that this method has good applicability to the GCPs on road traffic signs, and the accuracy rate is over 95%.