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In this paper, we extended video stitching to nearshore bathymetry for videos that were captured for the same coastal field simultaneously by two unmanned aerial vehicles (UAVs). In practice, a video captured by a single UAV often shows a limited coastal zone with a lack of a wide field of view. To solve this problem, we proposed a framework in which video stitching and bathymetric mapping were performed in sequence. Specifically, our method listed the video acquisition strategy and took two overlapping videos captured by two UAVs as inputs. Then, we adopted a unified video stitching and stabilization optimization to compute the stitching and stabilization of one of the videos separately. In this way, we can obtain the best stitching result. At the same time, background feature points identification on the shore plays the role of short-time visual odometry. Through the obtained panoramic video in Shuang Yue Bay, China, we used the temporal cross-correlation analysis based on the linear dispersion relationship to estimate the water depth. We selected the region of interest (ROI) area from the panoramic video, performed an orthorectification transformation and extracted time-stack images from it. The wave celerity was then estimated from the correlation of the signal through filtering processes. Finally, the bathymetry results were compared with the cBathy. By applying this method to two UAVs, a wider FOV was created and the surveying area was expanded, which provided effective input data for the bathymetry algorithms. [ABSTRACT FROM AUTHOR]

Although geologically constrained sandy beaches are ubiquitous along wave-exposed coasts, there is still a limited understanding of their morphological response, particularly under storm conditions, which is mainly due to a critical lack of nearshore bathymetry observations. This paper examines the potential to derive bathymetries from video imagery under challenging wave conditions in order to investigate headland control on morphological beach response. For this purpose, a video-based linear depth inversion algorithm is applied to three consecutive weeks of frames collected during daylight hours from a single fixed camera located at La Petite Chambre d'Amour beach (Anglet, SW France). Video-derived bathymetries are compared against in situ topo-bathymetric surveys carried out at the beginning and end of the field experiment in order to assess the performance of the bathymetric estimates. The results show that the rates of accretion/erosion within the surf zone are strongly influenced by the headland, whereas the beach morphological response can be classified into three main regimes depending on the angle of wave incidence θ p : (1) under deflection configuration ( θ p > 0 ° ), the alongshore sediment transport was trapped at the updrift side of the headland, promoting sand accretion. (2) Under shadowed configuration ( θ p < 0 ° ), the interruption of the longshore current drove a deficit of sand supply at the downdrift side of the headland, leading to an overall erosion in the surf zone. (3) Under shore-normal configuration ( θ p = 0 ° ), rip channels developed, and up-state beach transition was observed. A comparison between video-derived bathymetries and surveys shows an overall root mean square error (RMSE) around 0.49 to 0.57 m with a bias ranging between −0.36 and −0.29 m. The results show that video-derived bathymetries can provide new insight into the morphological change driven by storm events. The combination of such inferred bathymetry with video-derived surface current data is discussed, showing great potential to address the coupled morphodynamics system under time-varying wave conditions. [ABSTRACT FROM AUTHOR]

Estimation of nearshore bathymetry is important for accurate prediction of nearshore wave conditions. However, direct bathymetry data collection is expensive and time-consuming while accurate airborne lidar-based survey is limited by breaking waves and decreased light penetration affected by water turbidity. Instead, tower-based platforms or Unmanned Aircraft System (UAS) can provide indirect video-based observations such as time-series (or videos) and time-averaged (Timex) or variance enhanced (Var) images. The time-series imagery can provide wave celerity information for bathymetry estimation through the well-known dispersion relationship, for example the cBathy algorithm, or physics-based models. However, wave celerities and associated inverted water depths are sensitive to noise during image collection and processing stages or may not even be available over the entire area of interest. Timex or Var images can be used to identify persistent regions of wave breaking (for example over the sand bar and at the shoreline) so that one can create bathymetry profiles using simplified approximations based on parametric forms. However, the accuracy of this approach highly depends on the assumption of the chosen parametric form as well as the accuracy of detecting sandbars and shoreline. In this work, we propose a rapid and improved bathymetry estimation method that takes advantage of image-derived wave celerity from cBathy and a first-order bathymetry estimate from Parameter Beach Tool (PBT), software that fits parameterized sandbar and slope forms to the nearshore imagery. Two different sources of the data, PBT and wave celerity, are combined or blended optimally based on their assumed accuracy in a statistical (i.e., Bayesian) framework. The PBT-derived bathymetry serves as "prior" coarse-scale background information and then is updated and corrected with the cBathy-derived wave data through the dispersion relationship, which results in a better bathymetry estimate that is consistent with imagery-based wave data. To illustrate the accuracy of our proposed method, imagery data sets collected in 2017 at the US Army Engineer Research and Development Center's (ERDC) Field Research Facility (FRF) in Duck, North Carolina under different weather and wave height conditions are tested. Estimated bathymetry profiles are remarkably close to the direct survey data due to the optimal fusion of two data sets. The computational time for the estimation from PBT-based bathymetry and CBathy-derived wave celerity is only about five minutes on a free Google Cloud node with one CPU core. These promising results indicate the feasibility of reliable real-time bathymetry imaging during a single flight of UAS. • A novel method that can improve bathymetry estimation based on widely used cBathy. • Coastal images processed for background (Parametric Beach Tool) & celerity (cBathy). • The method blends the background bathymetry and celerity data into improved estimate. • The method tested with FRF data sets under different weather/wave height conditions. • The method is fast and scalable with a Python package for reproducible research. [ABSTRACT FROM AUTHOR]

In this paper, we extended video stitching to nearshore bathymetry for videos that were captured for the same coastal field simultaneously by two unmanned aerial vehicles (UAVs). In practice, a video captured by a single UAV often shows a limited coastal zone with a lack of a wide field of view. To solve this problem, we proposed a framework in which video stitching and bathymetric mapping were performed in sequence. Specifically, our method listed the video acquisition strategy and took two overlapping videos captured by two UAVs as inputs. Then, we adopted a unified video stitching and stabilization optimization to compute the stitching and stabilization of one of the videos separately. In this way, we can obtain the best stitching result. At the same time, background feature points identification on the shore plays the role of short-time visual odometry. Through the obtained panoramic video in Shuang Yue Bay, China, we used the temporal cross-correlation analysis based on the linear dispersion relationship to estimate the water depth. We selected the region of interest (ROI) area from the panoramic video, performed an orthorectification transformation and extracted time-stack images from it. The wave celerity was then estimated from the correlation of the signal through filtering processes. Finally, the bathymetry results were compared with the cBathy. By applying this method to two UAVs, a wider FOV was created and the surveying area was expanded, which provided effective input data for the bathymetry algorithms.