Geo-referencing remote images for precision agriculture using artificial terrestrial targets

The aim of this paper is to assess co-registration errors in remote imagery through the AUGEO system, which consists of geo-referenced coloured tarps acting as terrestrial targets (TT), captured in the imagery and semi-automatically recognised by AUGEO2. 0® software. This works as an add-on of ENVI® for image co-registration. To validate AUGEO, TT were placed in the ground, and remote images from satellite Quick Bird (QB), airplanes and unmanned aerial vehicles (UAV) were taken at several locations in Andalusia (southern Spain) in 2008 and 2009. Any geo-referencing system tested showed some error in comparison with the Differential Global Positioning System (DGPS)-geo-referenced verification targets. Generally, the AUGEO system provided higher geo-referencing accuracy than the other systems tried. The root mean square errors (RMSE) from the panchromatic and multi-spectral QB images were around 8 and 9 m, respectively and, once co-registered by AUGEO, they were about 1.5 and 2.5 m, for the same images. Overlapping the QB-AUGEO-geo-referenced image and the National Geographic Information System (NGIS) produced a RMSE of 6.5 m, which is hardly acceptable for precision agriculture. The AUGEO system efficiently geo-referenced farm airborne images with a mean accuracy of about 0.5-1.5 m, and the UAV images showed a mean accuracy of 1.0-4.0 m. The geo-referencing accuracy of an image refers to its consistency despite changes in its spatial resolution. A higher number of TT used in the geo-referencing process leads to a lower obtained RMSE. For example, for an image of 80 ha, about 10 and 17 TT were needed to get a RMSE less than about 2 and 1 m. Similarly, with the same number of TT, accuracy was higher for smaller plots as compared to larger plots. Precision agriculture requires high spatial resolution images (i.e., <1.5 m pixel-1), accurately geo-referenced (errors <1-2 m). With the current DGPS technology, satellite and airplane images hardly meet this geo-referencing requirement; consequently, additional co-registration effort is needed. This can be achieved using geo-referenced TT and AUGEO, mainly in areas where no notable hard points are available. © 2011 Springer Science+Business Media, LLC.

This research was partially financed by the Spanish Ministry of Science and Innovation through the projects AGL2007-60926 and AGL2010-15506.

Peer Reviewed