Geo-Referenced Digital Data Acquisition and Processing System Using LiDAR Technology

Terrain modeling usually requires some type of interpolation between the measured terrain points. Using modern airborne and spaceborne sensors, terrain point observations are becoming available in a variety of formats with widely differing characteristics. More importantly, the point distribution, accuracy and density can vary greatly, depending mostly on the sensor type and the data acquisition method used. This paper analyzes and compares the most important methods for terrain modeling, in particular, in the case of low point density and irregular point distribution. A new method is proposed for surface modeling, which is based on a two-dimensional Fourier series interpolation combined with a polynomial extension. The Fourier series technique as a powerful method to describe 1D/2D signals has been widely used in several diverse applications, including surface modeling. However, because it is based on evenly spaced input data it cannot be directly applied for surface approximations from irregularly spaced points. The proposed approach, therefore, aims to determine the coefficients in the case of uneven point distributions. The key idea of the concept is that a least squares adjustment is formulated to obtain the Fourier series coefficients. Since the Fourier series cannot model surface trends well, a polynomial extension has been added to model the surface trend, thus splitting the approximation task into two parts, global trend and local variation modeling. The proposed surface modeling method has shown promising performance even in the case of low point density and scattered point distribution.