Point clouds reduction model based on 3D feature extraction Online publication date: Tue, 29-Jan-2019
by Hadeer M. Sayed; Shereen A. Taie; Reda A. El-Khoribi; Ibraheem F. Abdelrahman; A.K. Helmy
International Journal of Embedded Systems (IJES), Vol. 11, No. 1, 2019
Abstract: Light detection and ranging (LIDAR) is a remote sensing method that scans the Earth's surface with high density to construct the digital elevation model (DEM). In this paper, we present a point clouds reduction model based on two 3D feature extraction techniques, namely: the sharp feature detection algorithm and feature extraction technique-based LIDAR point attributes. These techniques are used as initial selection criteria and are compared with the maximum and the minimum elevation criterion that gives reduction with the highest accuracy. However, point clouds reduction algorithms lead to high consumption of time to generate a reduced file with high accuracy, which prompts the need to propose a new model that considers the trade-off between the processing time and the accuracy. The results showed that the proposed model significantly reduced the processing time at the expense of accuracy reduction by 0.7% and 1.3% for the two used techniques respectively, which is acceptable for realistic applications.
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