Terrain classification based on adaptive weights with airborne LiDAR data for mining area
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.
mining subsidence airborne LiDAR strip division adaptive weights
LI Hui-ying WANG Zhi SUN Ya-feng LI Wen-hui
College of Computer Science and Technology, Jilin University, Changchun 130012, China College of Resources and Civil Engineering, Northeastern University, Shenyang 110004, China
国际会议
河南焦作
英文
648-653
2011-06-18（万方平台首次上网日期，不代表论文的发表时间）