| 基于深度学习和遥感影像的露天矿自动提取方法研究 |
| |
| 引用本文： | 刘发发,' target='_blank'>,韩红太,' target='_blank'>,张 敏,' target='_blank'>,麻连伟,' target='_blank'>.基于深度学习和遥感影像的露天矿自动提取方法研究[J].中州煤炭,2021,0(6):82-85，262. |
| |
| 作者姓名： | 刘发发 ' target='_blank'> 韩红太 ' target='_blank'> 张 敏 ' target='_blank'> 麻连伟 ' target='_blank'> |
| |
| 作者单位： | （1.河南省地球物理空间信息研究院，河南 郑州 450009； 2.河南省地质物探工程技术研究中心，河南 郑州 450009） |
| |
| 摘 要： | 非法开采不仅危害国家资源，威胁国家财产安全，也存在重大安全隐患，寻找快速发现非法开采行迹的解决办法迫在眉睫。利用光学遥感影像进行人工解译，费时费力、效率极低；而传统的露天矿遥感自动解译方法，或基于像素，或基于面向对象，利用的图像特征简单且数量较少。将深度学习的全卷积神经网络算法引入露天矿自动提取中，充分从底层特征中挖掘大量高层抽象特征，实现露天矿智能高效解译。实验结果表明，该方法在一定程度上有效提高了露天矿识别的准确率，能够为及时发现非法露天矿开采提供基础的数据技术支持。
|
| 关 键 词： | 深度学习 全卷积神经网络 光学遥感影像 露天矿 自动提取 |
Research on automatic extraction method of open-pit mine based on deep learning and remote sensing images |
| |
| Liu Fafa,' target='_blank'>,Han Hongtai,' target='_blank'>,Zhang Min,' target='_blank'>,Ma Lianwei,' target='_blank'>. Research on automatic extraction method of open-pit mine based on deep learning and remote sensing images[J].Zhongzhou Coal,2021,0(6):82-85，262. |
| |
| Authors: | Liu Fafa ' target='_blank'> Han Hongtai ' target='_blank'> Zhang Min ' target='_blank'> Ma Lianwei ' target='_blank'> |
| |
| Affiliation: | （1.Henan Institute of Geophysics and Spatial Information，Zhengzhou 450009，China；2.Henan Geological and Geophysical Exploration Engineering Technology Research Center，Zhengzhou 450009，China） |
| |
| Abstract: | Illegal mining occurs frequently，which not only endangers national resources and threatens the security of national property，but also may bring major security risks.
Therefore，it is extremely urgent to find a solution to early and quickly find the illegal mining track.The manual interpretation of optical remote sensing image is time-consuming and laborious，and the efficiency is extremely low.Traditional automatical open-pit mine interpretation in remote sensing method is either based on pixels，or object-oriented，which uses simple and limited image characteristics.In this paper we introduce the deep learning algorithm FCN into open-pit mine automatic extraction.This method can fully dig high-level features from the underlying ones，realizing the interpretation of open-pit mine intelligently and efficiently.The experimental results showed that，the method improves the accuracy of open-pit mine recognition in a certain extent，which will provide a fundenmental technical support for the timely detection of illegal open mining. |
| |
| Keywords: | ,deep learning, fully convolution networks, optical remote sensing images, open-pit mine, automatic extraction |
|
| 点击此处可从《中州煤炭》浏览原始摘要信息 |
|
点击此处可从《中州煤炭》下载全文 |
