The composition or bed material plays a crucial role in the physical hydromorphological processes of fluvial systems. However, conventional bed material sampling methods provide only pointwise information, which can be inadequate when investigating large rivers of inhomogeneous bed material characteristics. In this study, novel, image-based approaches are implemented to gain areal information of the bed surface composition using two different techniques: monocular and stereo computer vision. Using underwater videos, captured in shorter reaches of the Hungarian Danube River, a comparison of the bed material grain size distributions from conventional physical samplings and the ones reconstructed from the images is carried out. Moreover, an attempt is made to quantify bed surface roughness, using the so-called Structure from Motion image analysis method. Practical aspects of the applicability of image-based bed material mapping are discussed and future improvements towards an automatized mapping methodology are outlined.

中文翻译：

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基于图像的大河河床物质映射

成分或床层材料在河流系统的物理水文形态过程中起着至关重要的作用。然而，传统的河床物质采样方法仅提供逐点信息，这在调查具有不均匀河床物质特征的大河流时是不够的。在这项研究中，使用两种不同的技术：单目和立体计算机视觉，实现了新颖的、基于图像的方法来获取床表面成分的区域信息。使用在匈牙利多瑙河较短河段捕获的水下视频，对来自传统物理采样的床层材料粒度分布与从图像重建的床层材料粒度分布进行了比较。此外，还尝试量化床层表面粗糙度，使用所谓的结构来自运动图像分析方法。讨论了基于图像的床材料映射的适用性的实际方面，并概述了对自动化映射方法的未来改进。