基于高分二号卫星数据的煤矿区地质灾害信息提取研究

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。

Geological hazard information extraction in coal mine area based on GF-2 satellite data

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.