基于多模态特征融合的地质体识别方法  

Geological Body Recognition Based on Multi-Modal Feature Fusion

作　　者：付偲 李超岭[2] 张海燕[1,3] 刘畅 李丰丹[2] Fu Si;Li Chaoling;Zhang Haiyan;Liu Chang;Li Fengdan(School of Information Science&Technology,Beijing Forestry University,Beijing 100083,China;Development Research Center,China Geological Survey,Beijing 100037,China;Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration,Beijing 100083,China)

机构地区：[1]北京林业大学信息学院,北京100083 [2]中国地质调查局发展研究中心,北京100037 [3]国家林业草原林业智能信息处理工程技术研究中心,北京100083

出　　处：《地球科学》2023年第10期3743-3752,共10页Earth Science

基　　金：中国地质调查局地质调查项目(No.DD20190416).

摘　　要：将深度学习技术应用于地质填图,挖掘不同模态数据深层次信息,可以实现更为准确的地质填图.提出了一种基于多模态特征融合的地质体识别方法,综合考虑地球物理、地球化学数据和遥感影像数据,该方法先利用深度神经网络和卷积神经网络分别提取这两种不同模态数据的特征,然后进行特征拼接得到多模态特征,最后利用全连接神经网络进行特征融合完成地质体分类.交叉验证结果表明,提出的多模态特征融合方法比仅使用地球物理地球化学数据或遥感影像数据的深度学习方法相比有明显的优势,在分类准确率上分别提升了14.08%和2.79%,证明了该方法可以实现更为精准的地质体识别,进而更好地辅助地质填图.Applying deep learning technology to geological mapping to mine the deep-level in formation of different modal data,so as to achieve more accurate geological mapping.Considering the geophysical and geochemical data and remote sensing image data,in this paper it proposes a geological body recognition method based on multi-modal feature fusion.Firstly,Using deep neural network and convolution neural network to extract the features of the two different modal data and then performs feature splicing to obtain multi-modal features,finally,the fully connected neural network is used for feature fusion to complete the geological body classification.The cross-validation results show that the proposed multi-modal feature fusion method has obvious advantages compared with the deep learning methods using geophysical and geochemical data or remote sensing image data along,and the classification accuracy rate is increased by 14.08%and 2.79%.This result proves that this method can realize more accurate geological body identification,and then better assist geological mapping.

关 键 词：地质体识别 地质填图 多模态特征融合 深度学习 矿产勘察 

分 类 号：P623[天文地球—地质矿产勘探]