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Terrain classification based on adaptive weights with airborne LiDAR data for mining area

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摘要 The fast high-efficiency inspection for mining subsidence of mine area is a reliable way for forecasting accident and evaluating losing expense.In order to monitor mining subsidence of exploitation mine efficiently,LiDAR data were used and a novel strip division method was brought forward based on separating-treatment theory,which divided the mass of discrete three-dimensional point cloud data into a series of parallel strips and reduced the dimension in each strip.Polynomial fitting algorithm based on the adaptive weights,which located in the range of the strip,was used for classification complex terrain data of mine-area.The results show that LiDAR datamation can be greatly reduced.In the mean time,the time spending for calculation is shortened,and computational complexity is simplified.Therefore,high-efficiency terrain classification of LiDAR point cloud method can be great beneficial to monitoring environment of mine area.
出处 《中国有色金属学会会刊:英文版》 CSCD 2011年第S3期648-653,共6页 Transactions of Nonferrous Metals Society of China
基金 Project(2011CB707102)supported by the National Basic Research Program of China Projects(41001302,40901220)supported by the National Natural Science Foundation of China Project(200903190)supported by the Fundamental Research Funds for the Central Universities,China Project(20090450305)supported by the China Postdoctoral Science Foundation Project(122025)supported by the Fok Ying Tong Education Foundation,China

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