基于深度学习的岩体遥感智能解译模型研究——以黑龙江省苇河镇、亚布力镇、绥阳镇地区为例

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

在东北地区选取试验区,对比多种分类模型,提出一种基于多源多模态数据和多流CNN的岩体分类模型.其中包括两个子模型:一是基于大尺度邻域和深度卷积神经网络的岩体提取模型;二是基于波段组合和多模态数据的多流CNN融合模型.研究结果表明,预测结果图整体区域预测分布正确,总体精度评价指标达到84.4%,具有智能化程度高、客观性强的特点,能够为地质工作者提供辅助决策依据.此外,还采用迁移学习策略对样本数量进行扩容,解决了CNN模型小样本问题.

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.