Besides airborne laser bathymetry and multimedia photogrammetry, spectrally derived bathymetry provides a third optical method for deriving water depths. In this paper, we introduce BathyNet, an U-net like convolutional neural network, based on high-resolution, multispectral RGBC (red, green, blue, coastal blue) aerial images. The approach combines photogrammetric and radiometric methods: Preprocessing of the raw aerial images relies on strict ray tracing of the potentially oblique image rays, considering the intrinsic and extrinsic camera parameters. The actual depth estimation exploits the radiometric image content in a deep learning framework. 3D water surface and water bottom models derived from simultaneously captured laser bathymetry point clouds serve as reference and training data for both image preprocessing and actual depth estimation. As such, the approach highlights the benefits of jointly processing data from hybrid active and passive imaging sensors. The RGBC images and laser data of four groundwater supplied lakes around Augsburg, Germany, captured in April 2018 served as the basis for testing and validating the approach. With systematic depth biases less than 15 cm and a standard deviation of around 40 cm, the results satisfy the vertical accuracy limit Bc7 defined by the International Hydrographic Organization. Further improvements are anticipated by extending BathyNet to include a simultaneous semantic segmentation branch.

除了机载激光测深法和多媒体摄影测绘法外，光谱导出的测深法还提供了用于导出水深的第三种光学方法。在本文中，我们介绍了基于高分辨率，多光谱RGBC（红色，绿色，蓝色，沿海蓝色）的航拍图像，像类似卷积神经网络的U-net的BathyNet。该方法结合了摄影测量和辐射测量方法：原始航空图像的预处理依赖于潜在的倾斜图像光线的严格光线追踪，同时考虑了相机的固有和非固有参数。实际的深度估计会在深度学习框架中利用辐射图像的内容。从同时捕获的激光测深点云得出的3D水面和水底模型可作为图像预处理和实际深度估计的参考和训练数据。这样，该方法凸显了联合处理来自混合式主动和被动成像传感器的数据的好处。2018年4月捕获的德国奥格斯堡附近四个地下水湖泊的RGBC图像和激光数据是测试和验证该方法的基础。在系统深度偏差小于15厘米且标准偏差约为40厘米的情况下，结果满足了国际水文组织定义的垂直精度极限Bc7。通过将BathyNet扩展为包括同时语义分割分支，可以期待进一步的改进。2018年4月捕获的数据作为测试和验证该方法的基础。在系统深度偏差小于15 cm且标准偏差约为40 cm的情况下，结果满足了国际水文组织定义的垂直精度极限Bc7。通过将BathyNet扩展为包括同时语义分割分支，可以期待进一步的改进。2018年4月捕获的数据作为测试和验证该方法的基础。在系统深度偏差小于15 cm且标准偏差约为40 cm的情况下，结果满足了国际水文组织定义的垂直精度极限Bc7。通过将BathyNet扩展为包括同时语义分割分支，可以期待进一步的改进。