矿产与地质2024,Vol.38Issue(1) :184-194,204.DOI:10.19856/j.cnki.issn.1001-5663.2024.01.023

基于全卷积神经网络的遥感图像线性构造解译方法——以云县官房铜矿区为例

Linear structure interpretation method of remote sensing image based on full convolution neural network:An example of Guanfang copper mining area in Yunxian County

王宇翔 常河 王玉祥
矿产与地质2024,Vol.38Issue(1) :184-194,204.DOI:10.19856/j.cnki.issn.1001-5663.2024.01.023

基于全卷积神经网络的遥感图像线性构造解译方法——以云县官房铜矿区为例

Linear structure interpretation method of remote sensing image based on full convolution neural network:An example of Guanfang copper mining area in Yunxian County

王宇翔 1常河 1王玉祥1
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作者信息

  • 1. 昆明理工大学国土资源工程学院,云南昆明 650093
  • 折叠

摘要

文章研究了深度学习方法在地质构造解译中的应用,探究了相比传统的线性构造方法更为高效且无需先验知识的方法.以基于全卷积神经网络(FCN)的图像像素注释方法实现了遥感数据对于线性构造解译半自动解译.选择云南省云县官房铜矿矿区作为实验区域,绘制的图件表明该解译方法能够满足普通地质研究的基本需求,同时也能作为人工线性构造解译工作的初步参考,具有一定的研究意义.而与其他传统自动解译方法对比,可以发现在解译精度、效率和可重复使用性上都存在一定的优势.这些研究成果对于地质构造解译的自动化发展具有重要的参考价值,也为遥感解译智能化的发展提供了新思路.

Abstract

This paper investigates the application of deep learning method in geological structure interpreta-tion and explores for a more efficient and prior-knowledge-free approach compared to traditional linear con-struction method.An image pixel annotation method based on fully convolution neural network(FCN)is used to achieve semi-automated interpretation of linear structure with remote sensing data.Guanfang copper mining area in Yun County,Yunnan Province is selected as the experimental area,and the study result indi-cates that this interpretation method can meet the basic need of general geological research and serves as a preliminary reference for manual linear interpretation work with a certain research significance.Compared with other traditional automatic interpretation methods,it has the advantage in interpretation accuracy,effi-ciency and reusability.These research results have an important reference value for the automation develop-ment of geological structure interpretation,and it also provides a new idea for the intelligent development of remote sensing interpretation.

关键词

线性构造/全卷积神经网络/官房铜矿/语义分割

Key words

linear structure/full convolution neural network/Guanfang copper deposit/semantic segmentation

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基金项目

国家重点研发计划项目(2017YFC0602500)

出版年

2024
矿产与地质
桂林矿产地质研究院

矿产与地质

CSTPCD
影响因子:0.42
ISSN:1001-5663
参考文献量20
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