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地质科学  2018, Vol. 53 Issue (4): 1361-1374    DOI: 10.12017/dzkx.2018.079
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基于高分二号卫星数据的煤矿区地质灾害信息提取研究
赵    萍1,2,3,4    李志辉1,2,3,4    肖    瑶1,2,3,4    袁    峰1,2,3    李晓晖1,2,3,4
  1. 合肥工业大学资源与环境工程学院  合肥    230009
  2. 安徽省矿产资源与矿山环境工程技术研究中心,合肥工业大学  合肥    230009
  3. 矿床成因与勘查技术研究中心,合肥工业大学  合肥    230009
  4. 空间信息智能分析与应用研究所,合肥工业大学  合肥    230009
Geological hazard information extraction in coal mine area based on GF-2 satellite data
Zhao Ping1, 2, 3, 4    Li Zhihui1, 2, 3, 4    Xiao Yao1, 2, 3, 4    Yuan Feng1, 2, 3    Li Xiaohui1, 2, 3, 4
  1. School of Resources and Environmental Engineering, Hefei University of Technology, Hefei  230009
  2. Anhui Province Engineering Research Center for Mineral Resources and Mine Environments, Hefei University of Technology, Hefei  230009
  3. Ore Deposit and Exploration Center, Hefei University of Technology, Hefei  230009
  4. Institute for Intelligent Analysis and Application of Spatial Information, Hefei University of Technology, Hefei  230009
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摘要 

随着遥感数据获取技术和能力的全面提高,遥感数据呈现出明显的大数据特征。发展适应于遥感大数据的智能分析和信息挖掘技术,成为当前遥感技术研究的前沿。高分二号(GF-2)卫星数据是我国首颗自主研发的亚米级高分辨率卫星数据,具有观测幅宽、重访周期短、高辐射精度、高定位精度等优势,为未来我国地质灾害的长期、动态地监测和研究提供了高精度、稳定可靠的数据源。本文选取安徽谢桥煤矿2015年1月8日的GF-2卫星影像为研究数据,在对煤矿区主要地质灾害遥感地学分析的基础上,采用面向对象的影像分析方法对研究区由采煤活动所诱发的地质灾害信息进行自动提取。结果表明:利用GF-2卫星数据能够有效地识别地质灾害体的位置、范围、形态等空间分布特征;面向对象的自动提取方法对于煤矿区大面积的积水塌陷盆地、小规模的塌陷坑和线性的地裂缝都有很高的提取精度,识别精度达90% 以上;基于逐层剔除的思路构建的提取规则,为GF-2数据在地质灾害调查和大数据分析中的应用提供了很好的技术支持,也为其它地物目标的提取提供了参考,但在特征的选择和阈值的设定上需要具体分析。

 

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赵 萍 李志辉 肖 瑶 袁 峰 李晓晖
关键词遥感大数据   高分二号   煤矿区   地质灾害   地面塌陷   面向对象   信息提取     
Abstract

With the comprehensive improvement of remote sensing data acquisition technology and ability, remote sensing data shows obvious characteristics of big data. To develop the intelligent analysis and information mining technology for remote sensing big data has become the research front of remote sensing technology. The GF-2 satellite is the first self-developed satellite with submeter resolution in China. Furthermore, it has the advantages of width observation, short revisit period, high radiation precision and high positioning accuracy, which provides high precision, stable and reliable data source for the long-term dynamic monitoring and research of geological hazards in China. This paper selects the GF-2 satellite images of Anhui Xieqiao coal mine collected on January 8th in 2015 as the research data, based on the remote sensing geographical analysis of major geological hazards in coal mine area, the object-oriented image analysis method is used to automatically extract geological hazard induced by coal mining activities. The results show that the spatial distribution characteristics of geological hazard bodies can be effectively identified on GF-2 satellite data, such as the location, scope, shape; the object-oriented automatic extraction method has high accuracy, over 90%, for large seeper subsidence basins, small scale collapse pit and linear ground fissures in the coal mine area; The extraction rules constructed based on the concept of layer-by-layer rejection provide good technical support for the application of GF-2 data in geological hazard investigation and big data analysis in coal mine area, and also provide reference for the extraction of other objects. Howerver, the selection of features and the setting of thresholds need specific analysis according to the situation.

 

Key wordsRemote sensing big data   GF-2   Coal mine area   Geological hazard   Ground collapse   Object-oriented   Information extraction   
收稿日期: 2018-02-10;
基金资助:

国家重点研发计划项目(编号:2016YFC0600209)和国家自然科学基金项目(编号:41672069,41702353)资助。

作者简介: 赵 萍,女,1975年10月生,博士,副教授,地图学与地理信息系统专业。E-mail:njuzhp@sina.com
引用本文:   
赵 萍 李志辉 肖 瑶 袁 峰 李晓晖. 2018, 基于高分二号卫星数据的煤矿区地质灾害信息提取研究. 地质科学, 53(4): 1361-1374.
Zhao Ping Li Zhihui Xiao Yao Yuan Feng Li Xiaohui. Geological hazard information extraction in coal mine area based on GF-2 satellite data[J]. Chinese Journal of Geology, 2018, 53(4): 1361-1374.
 
没有本文参考文献
[1] 刘素红, 马建文, 燕守勋, 蔺启忠, 王元龙. 利用目标向量空间变换方法提取含矿岩层的研究——以新疆卡特里西地区为例[J]. 地质科学, 2000, (3): 370-375.
 
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