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Extracting mining subsidence land from RS images is one of important research contents for environment monitoring in mining area. The accuracy of traditional extracting models based on spectral features is low. In order to extract subsidence land from RS images with high accuracy, some domain knowledge should be imported and new models should be proposed. This paper, in terms of the disadvantage of traditional extracting models, imports domain knowledge from practice and experience, converts semantic knowledge into digital information, and proposes a new model for the specific task. By selecting Luan mining area as study area, this new model is tested based on GIS and related knowledge. The result shows that the proposed method is more pre- cise than traditional methods and can satisfy the demands of land subsidence monitoring in mining area.
Extracting mining subsidence land from RS images is one of important research contents for environment monitoring in mining area. The accuracy of traditional extracting models based on spectral features is low. In order to extract subsidence land from RS images with high accuracy, some domain knowledge should This paper, in terms of the disadvantage of traditional extracting models, imports domain knowledge from practice and experience, converts semantic knowledge into digital information, and proposes a new model for the specific task. By selecting Luan mining area as study area, this new model is tested based on GIS and related knowledge. The result shows that the proposed method is more pre- cise than traditional methods and may satisfy the demands of land subsidence monitoring in mining area.