Unmanned aerial vehicle (UAV) photogrammetry allows for the creation of
orthophotos and digital surface models (DSMs) of a terrain. However, DSMs of
water bodies mapped with this technique reveal water surface distortions,
preventing the use of photogrammetric data for accurate determination of water
surface elevation (WSE). Firstly, we propose a new solution in which a
convolutional neural network (CNN) is used as a WSE estimator from
photogrammetric DSMs and orthophotos. Second, we improved the previously known
"water-edge" method by filtering the outliers using a forward-backwards
exponential weighted moving average. Further improvement in these two methods
was achieved by performing a linear regression of the WSE values against
chainage. The solutions estimate the uncertainty of the predictions. This is
the first approach in which DL was used for this task. A brand new machine
learning data set has been created. It was collected on a small lowland river
in winter and summer conditions. It consists of 322 samples, each corresponding
to a 10 by 10 meter area of the river channel and adjacent land. Each data set
sample contains orthophoto and DSM arrays as input, along with a single
ground-truth WSE value as output. The data set was supplemented with data
collected by other researchers that compared the state-of-the-art methods for
determining WSE using an UAV. The results of the DL solution were verified
using k-fold cross-validation method. This provided an in-depth examination of
the model's ability to perform on previously unseen data. The WSE RMSEs differ
for each k-fold cross-validation subset and range from 1.7 cm up to 17.2 cm.
The RMSE results of the improved "water-edge" method are at least six times
lower than the RMSE results achieved by the conventional "water-edge" method.
The results obtained by new methods are predominantly outperforming existing
ones.

使用卷积神经网络估计无人机遥感获取影像的水域表面高程，并进行了改进，使用线性回归进一步提高准确度，将结果与现有方法进行了比较，得出了具有竞争力的结果。

利用无人机摄影测量和机器学习估算河流水面高程

Estimation of River Water Surface Elevation Using UAV Photogrammetry and  Machine Learning

The use of ground control points (GCPs) for georeferencing is the most common
strategy in unmanned aerial vehicle (UAV) photogrammetry, but at the same time
their collection represents the most time-consuming and expensive part of UAV
campaigns. Recently, deep learning has been rapidly developed in the field of
small object detection. In this letter, to automatically extract coordinates
information of ground control points (GCPs) by detecting GCP-markers in UAV
images, we propose a solution that uses a deep learning-based architecture,
YOLOv5-OBB, combined with a confidence threshold filtering algorithm and an
optimal ranking algorithm. We applied our proposed method to a dataset
collected by DJI Phantom 4 Pro drone and obtained good detection performance
with the mean Average Precision (AP) of 0.832 and the highest AP of 0.982 for
the cross-type GCP-markers. The proposed method can be a promising tool for
future implementation of the end-to-end aerial triangulation process.

该论文提出了一种基于 YOLOv5-OBB、置信度阈值过滤算法和最优排序算法的深度学习解决方案，用于自动提取航空影像中地面控制点坐标信息，取得了较好的检测效果，并可作为全流程航空三角测量的重要工具。