A new robust data comprssor for lidar data

Abstract

For a realistic representation of a terrain Light Detection and Ranging data (LiDAR) requires trillion numbers of points. These points connected in triangles that represent the surface of the terrain ultimately increase the data size. For online GIS interactive programs it has become highly essential to reduce the number of triangles in order to save more storing space. In this paper, it is extended to the LiDAR data compression. A newly developed data compression approach to approximate the LiDAR surface with a series of non-overlapping triangles has been presented. Generally a Triangulated Irregular Networks (TIN) are the most common form of digital surface model that consists of elevation values with x, y coordinates that make up triangles. Compression of TIN is needed for efficient management of large data and good surface visualization. This approach covers following steps: First, by using a Delaunay triangulation, an efficient algorithm is developed to generate TIN, which forms the terrain from an arbitrary set of data. A new interpolation wavelet filter for TIN has been applied in two steps, namely splitting and elevation. In the splitting step, a triangle has been divided into several sub-triangles and the elevation step has been used to 'modify' the point values (point coordinates for geometry) after the splitting. Then, this data set is compressed at the desired locations by using second generation wavelets. The quality of geographical surface representation after using proposed technique is compared with the original LIDAR data. The results show that this method can be used for significant reduction of data set.