Computer vision–based orthorectification and georeferencing of aerial image sets

Mohammad Reza Faraji; Xiaojun Qi; Austin Jensen

doi:10.1117/1.JRS.10.036027

22 September 2016 Computer vision–based orthorectification and georeferencing of aerial image sets

Mohammad Reza Faraji, Xiaojun Qi, Austin Jensen

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 10, Issue 3, 036027 (September 2016). https://doi.org/10.1117/1.JRS.10.036027

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.

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Mohammad Reza Faraji, Xiaojun Qi, and Austin Jensen "Computer vision–based orthorectification and georeferencing of aerial image sets," Journal of Applied Remote Sensing 10(3), 036027 (22 September 2016). https://doi.org/10.1117/1.JRS.10.036027

Published: 22 September 2016

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