Object oriented land cover classification using ALS and GeoEye imagery over mining area

摘要：

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 0.8914.

关键词： airborne laser scanning GeoEye nDSM object oriented classification mining areas

作者: YU Hai-yang CHENG Gang GE Xiao-san LU Xiao-ping

作者单位: Key Laboratory of Mine Spatial Information Technologies of State Bureau of Surveying and Mapping,Henan Polytechnic University, Jiaozuo 454000, China

会议类型: 国际会议

会议名称: 2011年国际矿山测量学术讨论会

会议地点: 河南焦作

会议语种:英文

页码: 733-737

在线出版日期: 2011-06-18（万方平台首次上网日期，不代表论文的发表时间）