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。