Refinement of Filtered Lidar Data Using Local Surface Properties

Since the introduction of lidar technology, lidar data has been used in a wide range of applications to generate quality surface models. Accordingly, because of the importance of terrain surface models in the applications, rigorous studies have been provided to extract ground points from a mixture of ground and nonground points in a lidar point cloud. Although most filters have been shown to classify lidar points successfully with their filter parameters tuned well, however, experiments revealed that there exist certain limitations in optimizing filter parameters and the correction of all misclassified points is not a straightforward task. In this study, therefore, we propose a method to improve the quality of filtered lidar data in an automated way, which exploits surface properties occurring between immediate neighbors. The method consists of a sequence of procedures which can reduce commission and omission errors. Commission errors which may occur in low-rise objects are designed to be reduced by utilizing morphological operations. On the other hand, omission errors are reduced by adding missing points around step edges. Experimental results show that the qualities of filtered data were improved considerably with our proposed method.