Estimation of the Grain-Size Distribution Using Semisupervised Affinity Propagation

Abstract

The grain-size distribution can play an important role in the sediment movement and the bedload transport rate. However, it still remains an important and challenging issue in the study of river behavior. Accurate estimation of the grain-size distribution is desired, while simultaneously one expects to spend much less time on it. Recently image analysis and machine learning techniques facilitated grain identification and measurement on images. In this paper, a semisupervised affinity propagation model (SAPM) oriented to images method is proposed for automatic extraction of the grain-size distribution based on photographs sampled from Wenchuan and Yingxiu in China where landslides and mudslides usually take place. The model to estimate the grain-size distribution is developed and the corresponding algorithm is illustrated in detail. The experiments are finished in both lab and field, and the proposed algorithm is compared with traditional methods. The proposed algorithm produces much better results in estimating the grain-size distribution in comparison with other image processing methods and manual sieving methods. It is shown that SAPM is an efficient method for precisely estimating the grain-size distribution.