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.