Classification and filtering of laser data

A strategy for the classification of raw LIDAR data as terrain, buildings and vegetation is presented. Its main features are a preliminary classification of grid data based on a geometric and topological description and a final filtering of raw data, guided by the previous classification. After raw data have been interpolated to a grid and segmented in connected regions bordered by a step edge, the topology of these regions is built up. Noise, vegetation and data gaps are classified first, mainly based on size and region fragmentation. Then, regions enclosing terrain and building points are labelled analysing their relationships with adjacent regions. Since regions may enclose more than one instance of different classes, a first check is made on grid data looking for consistency of gradient orientation with class characteristics. Finally, a local analysis is performed on each grid cell to label raw data point, based on the information on the surroundings inferred by the classification. Results obtained with Toposys and Optech systems on datasets with different ground point density gathered over the town of Pavia are shown to illustrate the effectiveness of the procedure.