REMOTE SENSING INTELLIGENT INTERPRETATION MODEL FOR ROCK MASS BASED ON DEEP LEARNING: A Case Study of Weihe Town, Yabuli Town and Suiyang Town in Heilongjiang Province

A rock mass classification model based on multisource and multimodal data and multistream convolutional neural network(CNN) is proposed for the selected test areas in Northeast China with comparison of various other models. The model includes two submodels: the rock mass extraction model based on large-scale neighborhood and deep convolutional neural network(DCNN) and multistream CNN fusion model based on band combination and multimodal data. The application shows that the whole regional predicted distribution in the forecast result map is correct, with the overall accuracy evaluation index reaching 84.4%, characterized by high intelligence and strong objectivity, which can provide auxiliary decision-making basis for geologists. Besides, transfer learning strategy is used to expand the number of samples to solve the small sample problem of CNN model.