A point-based adaptive filtering algorithm for lidar data classification in urban environment
You Shao, Samsung Lim
Last modified: 2016-09-13
Abstract
Over the last decade, many filtering algorithms have been developed to classify lidar point clouds. As a result, interpolation-based filters, slope-based filters and morphological filters have been widely accepted. Most of the filtering algorithms require the raw lidar data to be rasterized, however, rasterization often causes a significant loss of information. To overcome the information loss, we propose an adaptive filtering algorithm that classifies lidar data effectively into ground points and non-ground points in urban areas. The test results show that, by using an adaptive window size indicator, the proposed algorithm can classify more than 96% of ground points with an accuracy of 0.4 m in typical urban areas, and more than 90% of ground points in areas where complicated buildings are present.
Keywords
airborne lidar; adaptive filtering; morphological filter; rasterisation;
References
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