Surface classification from airborne laser scanning data

The high point density of airborne laser mapping systems enables achieving a detailed description of geographic objects and of the terrain. Growing experience shows, however, that extracting information directly from the data is practically impossible. This applies to basic tasks like Digital Elevation Model (DEM) generation and to more involved ones like the extraction of objects or generation of 3D city models. This paper presents an algorithm for surface clustering and for identifying structure in the laser data. The proposed approach concerns analyzing the surface texture, and via unsupervised classification identifying segments that exhibit homogeneous behavior. Clustering involves analysis of several key issues in relation to processing laser data such as different point densities, processing an irregularly distributed data set, analysis of attributes that can be derived from the data set, and ways to extract attributes. This paper provides a detailed discussion of these issues as well.