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Automatic morphological filtering algorithm for airborne lidar data in urban areas

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Abstract

Filtering is a key step for most airborne lidar post-applications in urban areas. To solve the problems of complex parameter settings and low filtering accuracy in complicated urban environments, an automatic morphological filter was proposed. In this paper, the optimal maximum filtering window can be determined automatically by applying a series of morphological top-hat operations. Meanwhile, the thresholds for filtering were calculated adaptively according to the gradient changes. Seven publicly available data sets provided by the International Society for Photogrammetry and Remote Sensing were used to evaluate the performance. Experimental results show that the proposed method achieved an average total error of 4.07% and an average kappa coefficient of 90.90%, which are the best performances when compared to some other filtering methods.

© 2019 Optical Society of America

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