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.