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Airborne Feature Matching Velocimetry for surface flow measurements in rivers

Author(s): Danxun Li; Liekai Cao; Volker Weitbrecht; Martin Detert

Linked Author(s): Martin Detert, Volker Weitbrecht

Keywords: Feature detection; Field study; Particle Image Velocimetry (PIV); Speeded-Up Robust Features (SURF); Structure from Motion (SfM); Unmanned Aerial Vehicle (UAV); Velocimetry

Abstract: A new airborne river surface flow measurement technique is presented, called Airborne Feature Matching Velocimetry (AFMV). It uses matchings of characteristic image features for orthorectification and velocimetry. Riparian matching points with an arbitrarily chosen base image serve to find individual projective transformation matrices to stabilize airborne video recordings. Transformed matching points’ distances of feature shifts between subsequent video frames lead to surface velocity vectors. To test this approach, a riverine moving water surface was recorded by an airborne video camera. Results are compared to (i) image frames rectified by 3D photogrammetry and (ii) related velocimetry results obtained by Particle Image Velocimetry (PIV). Generally, both time averaged and instantaneous surface velocities obtained by AFMV are shown to be of almost equal quality to the PIV approach. AFMV gives slightly less spatially-dense results in poorly textured areas, but clearly outperforms the 3D photogrammetry reference method in relation to computational power. Thus, the new method bears the potential to provide almost real-time instantaneous airborne river surface flow measurements.

DOI: https://doi.org/10.1080/00221686.2020.1818309

Year: 2021

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