An object oriented coal mining land cover classification method based on semantically meaningful image segmentation and image combination of GeoEye imagery and airborne laser scanning (ALS) data was presented. First, ...An object oriented coal mining land cover classification method based on semantically meaningful image segmentation and image combination of GeoEye imagery and airborne laser scanning (ALS) data was presented. First, DEM, DSM and nDSM (normalized Digital Surface Model, nDSM) were extracted from ALS data. The GeoEye imagery and DSM data were combined to create segmented objects based on neighbor regions merge method. Then 10 kinds of objects were extracted. Different kinds of vegetation objects, including crop, grass, shrub and tree, can be extracted by using NDVI and height value of nDSM. Water and coal pile field was extracted by using NDWI and the standard deviation of DSM method. Height differences also can be used to distinguish buildings from road and vacant land, and accurate building contour information can be extracted by using relationship of neighbor objects and morphological method. The test result shows that the total classification accuracy of the presented method is 90.78% and the kappa coefficient is 0.891 4.展开更多
To investigate geological mining hazards using digital techniques such as highresolution remote sensing,a semi-automatically geological mining hazards extraction method is proposed based on the case of the Shijiaying...To investigate geological mining hazards using digital techniques such as highresolution remote sensing,a semi-automatically geological mining hazards extraction method is proposed based on the case of the Shijiaying coal mine,located in Fangshan District,Beijing,China.In the method,the vegetation is first removed using the normalized difference vegetation index(NDVI)on the GeoEye-1 data.Then,geological mining hazards interpretation features are determined after color enhancement using principal component analysis(PCA)transformation.Bitmaps mainly covered by geological mining hazards are isolated by masking operation in the environment for visualizing images software.Next,each bitmap is classified into a two-valued imagery using support vector machine algorithm.In the two-valued imagery,1 denotes the geological mining hazards,while 0 denotes none.Afterwards,the two-valued imagery is converted into a vector graph by corresponding functions in the ArcGIS software and no geological mining hazards regions in the vector graph are deleted manually.Finally,the correlation between factors(such as mining activity,lithology,geological structure,and slope)and geological mining hazards is analyzed using a logistic regression and a hazardous-area forecasting model is built.The results of field verification show that the accuracy of the geological mining hazards extraction method is 98.1%and the results of the hazardous-area forecasting indicate that the logistic regression is an effective model in assessing geological hazard risks and that mining activity is the main contributing factor to the hazards,while geological structure,slope,lithology,roughness of the surface,and aspect are the secondary.展开更多
基金Project(2009CB226107)supported by the National Basic Research Program of China
文摘An object oriented coal mining land cover classification method based on semantically meaningful image segmentation and image combination of GeoEye imagery and airborne laser scanning (ALS) data was presented. First, DEM, DSM and nDSM (normalized Digital Surface Model, nDSM) were extracted from ALS data. The GeoEye imagery and DSM data were combined to create segmented objects based on neighbor regions merge method. Then 10 kinds of objects were extracted. Different kinds of vegetation objects, including crop, grass, shrub and tree, can be extracted by using NDVI and height value of nDSM. Water and coal pile field was extracted by using NDWI and the standard deviation of DSM method. Height differences also can be used to distinguish buildings from road and vacant land, and accurate building contour information can be extracted by using relationship of neighbor objects and morphological method. The test result shows that the total classification accuracy of the presented method is 90.78% and the kappa coefficient is 0.891 4.
基金This research was supported by the National Basic Research Program of China(973 Program,No.2009CB723906)National Natural Science Foundation of China(No.41171280)CEODE Program(No.DESP01-04-10).
文摘To investigate geological mining hazards using digital techniques such as highresolution remote sensing,a semi-automatically geological mining hazards extraction method is proposed based on the case of the Shijiaying coal mine,located in Fangshan District,Beijing,China.In the method,the vegetation is first removed using the normalized difference vegetation index(NDVI)on the GeoEye-1 data.Then,geological mining hazards interpretation features are determined after color enhancement using principal component analysis(PCA)transformation.Bitmaps mainly covered by geological mining hazards are isolated by masking operation in the environment for visualizing images software.Next,each bitmap is classified into a two-valued imagery using support vector machine algorithm.In the two-valued imagery,1 denotes the geological mining hazards,while 0 denotes none.Afterwards,the two-valued imagery is converted into a vector graph by corresponding functions in the ArcGIS software and no geological mining hazards regions in the vector graph are deleted manually.Finally,the correlation between factors(such as mining activity,lithology,geological structure,and slope)and geological mining hazards is analyzed using a logistic regression and a hazardous-area forecasting model is built.The results of field verification show that the accuracy of the geological mining hazards extraction method is 98.1%and the results of the hazardous-area forecasting indicate that the logistic regression is an effective model in assessing geological hazard risks and that mining activity is the main contributing factor to the hazards,while geological structure,slope,lithology,roughness of the surface,and aspect are the secondary.
