Estimation of Sediment Grain Size Distribution Using Optical Image-Based Spatial Feature Representation Learning with Data Augmentation

Abstract

This study introduces a spatial encoder network designed to estimate sand size distribution from optical images of sediments. The model achieves sufficient network capacity by stacking two-dimensional convolution-based encoder blocks to learn the spatial features that relate sediment images to grain size distribution. Additionally, to improve robustness and reliability, data augmentation techniques, including horizontal and vertical flipping, are used during training. The proposed model was applied to 41 littoral systems located along the eastern coast of the Korean Peninsula and was developed using grain size distribution data through sieve analysis and images obtained from 2010 to 2024. The proposed model demonstrated an impressive correlation of 98% for the estimated mean diameter of grain size and improved root mean square error across all measures of grain size distribution when compared to previous deep learning-based methods. The improvement in the accuracy of grain size distribution estimation using the proposed image-based deep learning model is expected to contribute to the advancement of conventional approaches, which are labor-intensive and time-consuming.