Automating Ground Control Point Detection in UAS Imagery Using Matrix Barcodes

Small unmanned aircraft systems (UAS) are increasingly used for remote sensing applications in precision agriculture due to their ability to collect high-resolution imagery. However, spatial calibration of UAS imagery is often a manual process that requires extensive planning and post-processing, presenting bottlenecks for automating image analysis workflows. This study seeks to address bottlenecks in the photogrammetry workflow that arise from manually tagging ground control points (GCPs) by automating the process. The main objectives included investigating (1) the application of data compression techniques for global navigation satellite system (GNSS) coordinates in generating matrix barcode representations and (2) the recovery of GNSS coordinates from matrix barcodes using a small UAS. GNSS coordinates were compressed using a base-36 encoding schema and encoded into QR code GCPs to reduce the number of alphanumeric characters required. Preliminary in-field testing demonstrated the reliability of recovering QR code GCPs from aerial imagery across various altitudes and exposure settings, with adjustments in exposure compensation mitigating altitude-related recoverability issues. Moreover, results indicated that the processing of aerial imagery into orthomosaic images did not compromise QR code recoverability. Further in-field testing identified QR code GCP background color as a key factor influencing recoverability, with darker colors generally improving recoverability. Statistical analysis validated altitude and background color as significant predictors of QR code GCP recoverability. Future research avenues include incorporating environmental factors such as solar radiation to improve statistical model fit. Overall, QR code GCPs offer a potential approach for automating photogrammetry workflows, reducing both time and labor associated with manual tagging.