Optimizing Uas Image Acquisition and Geo-Registration for Precision Agriculture

Unmanned Aircraft Systems (UASs) can acquire imagery of crop fields in various spectral bands, including the visible, near-infrared, and thermal portions of the spectrum. By combining techniques of computer vision, photogrammetry, and remote sensing, these images can be stitched into precise, geo-registered maps, which may have applications in precision agriculture and other industries. However, the utility of these maps will depend on their positional accuracy. Therefore, it is important to quantify positional accuracy and consider the tradeoffs between accuracy, field site setup, and the computational requirements for data processing and analysis. This will enable planning of data acquisition and processing to obtain the required accuracy for a given project.

This study focuses on developing and evaluating methods for geo-registration of raw aerial frame photos acquired by a small fixed-wing UAS. This includes visual, multispectral, and thermal imagery at 3, 6, and 14 cm/pix resolutions, respectively. The study area is 10 hectares of soybean fields at the Agronomy Center for Research and Education (ACRE) at Purdue University. The dataset consists of imagery from 6 separate days of flights (surveys) and supporting ground measurements. The Direct Sensor Orientation (DiSO) and Integrated Sensor Orientation (InSO) methods for geo-registration are tested using 16 Ground Control Points (GCPs). Subsets of these GCPs are used to test for the effects of different numbers and spatial configurations of GCPs on positional accuracy. The horizontal and vertical Root Mean Squared Error (RMSE) is used as the primary metric of positional accuracy. Preliminary results from 1 of the 6 surveys show that the DiSO method (0 GCPs used) achieved an RMSE in the X, Y, and Z direction of 2.46 m, 1.04 m, and 1.91 m, respectively. InSO using 5 GCPs achieved an RMSE of 0.17 m, 0.13 m, and 0.44 m. InSO using 10 GCPs achieved an RMSE of 0.10 m, 0.09 m, and 0.12 m. Further analysis will identify the optimal spatial configuration and number of GCPs needed to achieve sub-meter RMSE, which is considered a benchmark for precision agriculture purposes. Additional benefits of superior positional accuracy will also be explored.