GIS支持下遥感图象中采矿塌陷地提取方法研究

采矿塌陷地动态监测是工矿区资源管理与环境保护的重要方面 ,遥感技术可在其中发挥重要作用 ,从遥感图象中提取采矿塌陷地是遥感应用于矿山资源环境监测的重要研究课题 .传统的提取方法主要基于光谱特征 ,精度与效率都难以满足应用要求 ,为了以较高的精度 ,从遥感图象中提取塌陷地 ,必须建立新的方法与模型 .将遥感技术与 GIS相结合进行专题信息提取是有效的途径之一 .本文根据研究区的特点 ,以具体应用为指导 ,遥感技术与 GIS相结合 ,提出了 GIS支持下的分层分类、基于 GIS变化区域识别的分类、基于 GIS和领域知识对遥感分类图象进行后处理、GIS支持下采矿塌陷地的直接提取等方法与模型 ,充分应用光谱特征、地学特征与信息、领域和专家知识及其他统计数据辅助进行遥感图象处理与专题信息提取 .这些方法在精度、效率等方面均较传统方法有较大提高 ,最大提取精度可达到 89% ,能够有效地对工矿区土地塌陷态势进行动态监测

The Extraction of Mining Subsiding Land from RS Image Supported by GIS

It is one important aspect of RS applications in mining areas to extract mining subsiding land from RS images. The precision and efficiency of traditional methods based on spectral features is low. In order to extract mining subsiding land from RS images with high precision and efficiency, new methods and algorithms should be proposed. The integration of RS and GIS can be used to this filed, and GIS can support the RS image processing and information extraction. In this paper, according to the spectral and spatial properties of subsiding land in studying area and related knowledge, some new methods and models used to RS image processing and subsiding land extraction with the support of GIS are proposed, including layered classification supported by GIS, classification of changeable region identified by GIS, classified image post processing based on GIS and domain knowledge and direct extraction models based on GIS and domain knowledge. In those new methods, both GIS data and some spatial analysis functions are used to RS image classification and information extraction. GIS can serve as the auxiliary proof or direct information of classification, and provides check and comparison basis for the results, also it can be used as image processing platform. It proved that the methods supported by GIS could make full use of spectral features, Geo information and properties, domain and expert knowledge and other statistics data. Those new methods are more precise and effective than traditional methods, and the best precision can reach about 89%. So RS images can be used to monitor land subsidence situation effectively and dynamically.