Remote Sensing Applications and Decision Support

Computer vision–based orthorectification and georeferencing of aerial image sets

[+] Author Affiliations
Mohammad Reza Faraji, Xiaojun Qi

Utah State University, Department of Computer Science, 4205 Old Main Hill, Logan, Utah 84322-4205, United States

Austin Jensen

Utah State University, Utah Water Research Laboratory, 8200 Old Main Hill, Logan, Utah 84322-8200, United States

J. Appl. Remote Sens. 10(3), 036027 (Sep 22, 2016). doi:10.1117/1.JRS.10.036027
History: Received May 16, 2016; Accepted August 30, 2016
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Abstract.  Generating a georeferenced mosaic map from unmanned aerial vehicle (UAV) imagery is a challenging task. Direct and indirect georeferencing methods may fail to generate an accurate mosaic map due to the erroneous exterior orientation parameters stored in the inertial measurement unit (IMU), erroneous global positioning system (GPS) data, and difficulty in locating ground control points (GCPs) or having a sufficient number of GCPs. This paper presents a practical framework to orthorectify and georeference aerial images using the robust features-based matching method. The proposed georeferencing process is fully automatic and does not require any GCPs. It is also a near real-time process which can be used to determine whether aerial images taken by UAV cover the entire target area. We also extend this framework to use the inverse georeferencing process to update the IMU/GPS data which can be further used to calibrate the camera of the UAV, reduce IMU/GPS errors, and thus produce more accurate mosaic maps by employing any georeferencing method. Our experiments demonstrate the effectiveness of the proposed framework in producing comparable mosaic maps as commercial software Agisoft and the effectiveness of the extended framework in significantly reducing the errors in the IMU/GPS data.

© 2016 Society of Photo-Optical Instrumentation Engineers

Citation

Mohammad Reza Faraji ; Xiaojun Qi and Austin Jensen
"Computer vision–based orthorectification and georeferencing of aerial image sets", J. Appl. Remote Sens. 10(3), 036027 (Sep 22, 2016). ; http://dx.doi.org/10.1117/1.JRS.10.036027


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