Convolutional neural networks for water content classification and prediction with ground penetrating radar

Abstract

A novel ground penetrating radar (GPR)-based subsurface water content classification and prediction technique using deep neural networks is proposed. The fantastic advantages of deep network in classification and regression tasks show the huge potential to measure soil moisture content status quickly after the network trained. The technique is based on convolutional neural network (CNN), and does not need to extract data features in advance. We design two CNNs for the classification and regression tasks separately. Both networks can be divided into two parts: convolutional layers following max-pooling layers to extract features, and classifier or regressor part for output prediction. One network's output is a classification prediction (moisture quantitative classification), the other is prediction of a continuous variable (water content). We train the classification network firstly, and then use transfer learning technique to reuse the feature extraction part to train the regression network. With application of transfer learning, the regression task needs less training dataset and can achieve a good performance. Our method can be applied on many field, such as roadbed maintenance and continuous soil water content estimation.