| Title |
Framework for UAV-based river flow velocity determination employing optical recognition / |
| Authors |
Kriščiūnas, Andrius ; Čalnerytė, Dalia ; Akstinas, Vytautas ; Meilutytė-Lukauskienė, Diana ; Gurjazkaitė, Karolina ; Barauskas, Rimantas |
| DOI |
10.1016/j.jag.2024.104154 |
| Full Text |
|
| Is Part of |
International journal of applied earth observation and geoinformation.. Amsterdam : Elsevier. 2024, vol. 134, art. no. 104154, p. 1-13.. ISSN 1569-8432. eISSN 1872-826X |
| Keywords [eng] |
unmanned aerial vehicle ; aerial video ; river flow velocity ; optical flow ; indirect measurements |
| Abstract [eng] |
The determination of river velocity is important for hydromorphological analyses and river monitoring systems. Indirect measurements of river velocity using videos recorded by unmanned aerial vehicles (UAV) allow fast and cost-effective processing of information about the river stretch. This paper presents a method for computing flow velocity of the river surface using deep supervised model RAFT to determine the optical flow in combination with image pre-processing by convolutional operations. Moreover, the windiness coefficients and variance score were proposed to evaluate reliability of the collected data and the obtained results of optical flow detection. Various image pre-processing techniques were applied, namely the selection of the analysed area and the number of convolutional operations to select the one with the lowest variance score. This score represents the consistency of the river flow velocity during the video and can be used to filter out unreliable results. The numerical experiments were performed using the videos and directly measured velocity values of 4 shallow rivers in Lithuania collected during the field surveys. The optical velocity estimation method showed good correspondence to the directly measured values for the velocity range from 0 m/s to 0.8 m/s in the points with low variance score up to 0.192 that represents the first quartile of the variance. The optical flow method tends to underestimate the velocity up to 0.5 m/s for the quartiles with the higher variance scores. It was shown that in most cases the lowest variance score value was obtained using pre-processing techniques without convolutional operations. However, the need to analyse various pre-processing techniques arises from the different origin of the objects moving on the river surface. |
| Published |
Amsterdam : Elsevier |
| Type |
Journal article |
| Language |
English |
| Publication date |
2024 |
| CC license |
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