The Influence of Camera Calibration on Nearshore Bathymetry Estimation from UAV Videos

Abstract

Measuring the nearshore bathymetry is critical in coastal management and morphodynamic studies. The recent advent of Unmanned Aerial Vehicles (UAVs), in combination with coastal video monitoring techniques, allows for an alternative and low cost evaluation of the nearshore bathymetry. Camera calibration and stabilization is a critical issue in bathymetry estimation from video systems. This work introduces a new methodology in order to obtain such bathymetries, and it compares the results to echo-sounder ground truth data. The goal is to gain a better understanding on the influence of the camera calibration and stabilization on the inferred bathymetry. The results show how the proposed methodology allows for accurate evaluations of the bathymetry, with overall root mean square errors in the order of 40 cm. It is shown that the intrinsic calibration of the camera, related to the lens distortion, is the most critical aspect. Here, the intrinsic calibration that was obtained directly during the flight yields the best results.

Nearshore Bathymetry Based on ICESat-2 and Multispectral Images: Comparison Between Sentinel-2, Landsat-8, and Testing Gaofen-2

“…D ETAILED and accurate nearshore bathymetry is critical for a variety of applications, including offshore navigation, ocean research, and marine spatial management [1], [2]. With the expansion of human activities in coastal and marine areas, there is growing need to investigate the topography and physical features of the seafloor [3].…”

UCalib: Cameras Autocalibration on Coastal Video Monitoring Systems

“…In the case where the camera moves, the homography between different images is only valid when the points in the real world are placed over a common plane. For some beaches, it can be assumed that the surface is at the same height, as [24] does in a first estimation; however, in general, this approximation can introduce significant errors.…”

“…Whenever the water zone is of interest [e.g., for bathymetry inversion [21,24]], it is necessary that calibrations perform well at it. Whenever the horizon line is observable, errors ε H at the horizon give a hint of the performance of the calibration in the water zone, far from the GCPs for calibration, and also far from the features detected by the ORB algorithm (green dots in Figure 5C,F) for automatic calibration.…”

“…Images have also been used, in a quantitative way, to locate the shoreline and study its evolution [13][14][15], to determine the intertidal morphology [16][17][18], to estimate the wave period, celerity and propagation direction [19,20] and to infer bathymetries [21,22]. For these latter applications, in which magnitudes in physical space are required, the accurate georeferencing of images is essential [23,24].…”

“…In this paper, we explore image calibration by automatically identifying arbitrary features, i.e., without pre-selection, in the images to be calibrated and in previously calibrated images. Provided that fixed features will be considered very limited, it will not be possible to calibrate the images on the standard GCPs approach, as was done in [24]. Alternatively, we relate pixels of pairs of images through homographies, the main assumption of this work being that the position of the camera position is nearly invariant.…”