Computer vision–based orthorectification and georeferencing of aerial image sets

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.

Mini-Unmanned Aerial Vehicle-Based Remote Sensing: Techniques, applications, and prospects

“…Considering the inaccuracy of exterior orientation from GPS/IMU and the difficulties in acquisition of GCPs, another way for ortho-rectification is based on registration with the aerial/satellite orthorectified map [128]. In contrast, this way is more efficient and convenient due to the avoidance of complex aerial triangulation and DEM generation as well as the laborious acquisition of GCPs, however, its mandatory prerequisite is the reference maps.…”

Calibration and accuracy assessment in a direct georeferencing system for UAS photogrammetry

“…Photoscan is widely employed in scientific papers discussing aerial photogrammetry performed via manned and unmanned aircraft (Panayotov (2015), Turner, Lucieer, and Wallace (2014), Faraji, Qi, and Jensen (2016) etc.) because it implements stateof-the-art CV algorithms, ensures a clear workflow, offers multiple import and export options, and is reasonably priced.…”

Robust Mosaicking of Lightweight UAV Images Using Hybrid Image Transformation Modeling

“…Because this process may cause error propagation in pairwise transformations, an optimization method is required to minimize error accumulation. To this end, graph methods [23][24][25] and bundle adjustment methods [7,9,15,26,27] have been proposed. We adopted a modified graph method to ensure efficiency and robustness in image mosaicking.…”

“…Mehrdad et al [14] reduced tiepoint extraction regions using epipolar geometry established from initial camera parameters. Faraji et al [15] minimized the computation of tiepoint extraction using reference images in target areas. On the other hand, enhancing mosaicking accuracy was tried by minimizing the accumulated errors and distortions that are likely to occur in image transformation estimation.…”