Optimizing matrix barcode ground control points for automated location detection in UAS-based remote sensing

Abstract

High spatial accuracy is essential in unmanned aircraft system (UAS)-based remote sensing for applications in precision agriculture. Traditional ground control points (GCPs) improve the spatial accuracy of measurements derived from imagery but typically require manual tagging, a process that is both time-intensive and susceptible to errors. Matrix barcodes offer an opportunity to automate georeferencing in UAS-based remote sensing by addressing the bottlenecks associated with traditional GCPs in photogrammetry. This study aimed to refine the encoding process of matrix barcode GCPs, with objectives that included (1) evaluating the performance of several barcode types for use in GCP applications; and (2) assessing different compression methods for encoding geographic coordinates within the barcodes. Field tests showed that Aztec and Micro QR codes provided more reliable recovery at higher altitudes—up to 45 m above ground level compared to standard QR codes—attributed to their larger module sizes. Statistical analysis identified altitude and barcode type as significant factors impacting barcode recovery, with Aztec and Micro QR codes showing a 55–57 % higher recovery rate compared to standard QR codes for 1.07 x 1.07 m GCPs. While statistical analysis found no significant differences among encoding methods, field tests indicated that binary encoding had relatively improved performance, particularly when paired with Aztec codes. However, environmental data collection did not fully support one-to-one comparisons across different days; as such, the results should be interpreted within the specific conditions of this study rather than as definitive conclusions. Overall, these findings offer insights into the feasibility of using matrix barcodes as an automated alternative to traditional GCPs, potentially improving the efficiency of UAS-based remote sensing workflows by reducing the labor, time, and risk of user error associated with manual GCP processing.