Authors: Hadeer M. Sayed; Shereen A. Taie; Reda A. El-Khoribi; Ibraheem F. Abdelrahman; A.K. Helmy
Addresses: Faculty of Computers and Information, Fayoum University, Fayoum 63611, Egypt ' Faculty of Computers and Information, Fayoum University, Fayoum 63611, Egypt ' Faculty of Computers and Information, Cairo University, Giza 12511, Egypt ' Faculty of Computers and Information, Cairo University, Giza 12511, Egypt ' National Authority for Remote Sensing and Space Sciences, Cairo 11311, Egypt
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.
Keywords: light detection and ranging; LIDAR; digital elevation model; DEM; 3D features extraction; radial basis function; RBF; data reduction.
International Journal of Embedded Systems, 2019 Vol.11 No.1, pp.78 - 83
Available online: 22 Jan 2019 *Full-text access for editors Access for subscribers Free access Comment on this article