Classification of River Sediment Fractions in a River Segment including Shallow Water Areas Based on Aerial Images from Unmanned Aerial Vehicles with Convolution Neural Networks

Abstract

Riverbed materials serve multiple environmental functions as a habitat for aquatic invertebrates and fish. At the same time, the particle size of the bed material reflects the tractive force of the flow regime in a flood and provides useful information for flood control. The traditional riverbed particle size surveys, such as sieving, require time and labor to investigate riverbed materials. The authors of this study have proposed a method to classify aerial images taken by unmanned aerial vehicles (UAVs) using convolutional neural networks (CNNs). Our previous study showed that terrestrial riverbed materials could be classified with high accuracy. In this study, we attempted to classify riverbed materials of terrestrial and underwater samples including that which is distributed in shallow waters where the bottom can be seen using UAVs over the river segment. It was considered that the surface flow types taken overlapping the riverbed material on images disturb the accuracy of classification. By including photographs of various surface flow conditions in the training data, the classification focusing on the patterns of riverbed materials could be achieved. The total accuracy reached 90.3%. Moreover, the proposed method was applied to the river segments to determine the distribution of the particle size. In parallel, the microtopography was surveyed using a LiDAR UAV, and the relationship between the microtopography and particle size distribution was discussed. In the steep section, coarse particles were distributed and formed riffles. Fine particles were deposited on the upstream side of those riffles, where the slope had become gentler due to the dammed part. The good concordance between the microtopographical trends and the grain size distribution supports the validity of this method.

Keywords

• underwater
• particle size
• surface waves
• convolution neural network
• UAV