An improved technique for LIDAR data reduction

Abstract

Light detection and ranging (LIDAR) is a technology of remote imaging technologies. Currently, it is the most important technology for accruing elevation points with a high density in the form of digital elevation model (DEM) construction. However, the high-density data leads to time and memory consumption problems during data processing. In this paper, we depend on radial basis function (RBF) with Gaussian interpolation method to carry out LIDAR data reduction by select the most important points from the unprocessed data to remain the constructed DEMs with high accuracy as possible. Comparing the results with respect to the accuracy using Structural Similarity Index (SSIM) with Multiquadric and TPS interpolation methods. The results showing that Gaussian method is the most accurate method with 5.49% regardless each Multiquadric and TPS methods.