Ground and Non-Ground Filtering for Airborne LIDAR Data

Suchita Yadav, (doi: 10.23953/cloud.ijarsg.41)

Abstract


Automatic ground filtering for Light Detection and Ranging (LIDAR) data is a critical process for Digital Terrain Model and three-dimensional urban model generation. Various methods have been proposed in literature to separate ground from non-ground, but sometimes problem has been occurred due to the similar characteristics possessed by ground and non-ground objects. The proposed approach in this paper is based on neighborhood based approach. Hierarchy of preprocessing is done for LIDAR data using various essential tools. K-D tree is used to distinguish the bare ground and non-ground objects using nearest neighbor search. Experimental results show the effectiveness of the proposed approach.

 


Keywords


LiDAR, Airborne LiDAR, K-D tree, Point Clouds

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*2016 Journal Impact Factor was established by dividing the number of articles published in 2014 and 2015 with the number of times they are cited in 2016 based on Google Scholar, Google Search and the Microsoft Academic Search. If ‘A’ is the total number of articles published in 2014 and 2015, and ‘B’ is the number of times these articles were cited in indexed publications during 2016 then, journal impact factor = A/B. To know More: (http://en.wikipedia.org/wiki/Impact_factor)