GROSS ERROR DETECTION AND ANALYSIS BY HIERARCHICALCLASSIFICATION OF MOUNTAINOUS LIDAR DATA
Li Yun 1) ;Yang Zhiqiang 1) ; and Yang Bo 2)
1)Institute of Surveying and Spatial Information，Chang’an University，Xi’an 710054;2)China JK Institute of Engineering Investigation and Design, Xi’an 710043
Abstract Gross error detection is one of the important data processing steps of mountainous LIDAR point cloud data. Through analysing the features of gross error distribution,original LIDAR point cloud data can be divided into extreme outliers, outlier clusters and isolated points. On this basis, the idea of hierarchical gross error detection of mountainous LIDAR point cloud data is proposed, and an example of experimental data is verified. Experimental results show that the method can effectively remove gross errors from original mountainous LIDAR point cloud data, and, to a certain extent, improving the effect of preprocessing of point cloud.
Li Yun ,Yang Zhiqiang ,and Yang Bo . GROSS ERROR DETECTION AND ANALYSIS BY HIERARCHICALCLASSIFICATION OF MOUNTAINOUS LIDAR DATA[J]. , 2012, 32(2): 60-63.
Li Yun ,Yang Zhiqiang ,and Yang Bo . GROSS ERROR DETECTION AND ANALYSIS BY HIERARCHICALCLASSIFICATION OF MOUNTAINOUS LIDAR DATA[J]. jgg, 2012, 32(2): 60-63.
