Dual-input attention network for automatic identification of detritus from river sands

Abstract

Identifying the categories of detritus collected from river sands is an important work in geological researches, including sediment source analysis, tectonic evolution and lithofacies palaeogeography. Among deep learning techniques developed in recent years, Convolutional Neural Network (CNN) can be applied to the detritus identification problem. However, due to both data insufficiency caused by the high cost of manual labelling, and data imbalance caused by the uneven distribution of different categories of detritus, existing CNN models are hindered to reach their best performance. In this paper, we propose a novel network architecture for the problem of detritus identification: Dual-Input Attention Network (DANet), which accepts both plane-polarized images and cross-polarized images of detritus as input, and uses Parametrized Cross-Entropy as the loss function in order to alleviate the poor performance of detritus identification caused by data insufficiency and data imbalance. Experiments based on the detritus collected from the Yarlung Zangbo River Basin prove both the effectiveness and potential of DANet for detritus identification.