Classification of LIDAR data using a lower envelope follower and gradient-based operator

A new, computationally efficient classification methodology was developed and implemented to classify Light Detection and Ranging (LIDAR) data as ground, vegetation, and man-made features (Weed 2001). The new procedure consists of several components that create ground, vegetation, and building surfaces, which are then used to classify the first and last reflection of each laser pulse. Ground and non-ground data are classified by adapting the concept of a lower envelope follower used to recover information in an amplitude modulated (AM) signal to the problem of extracting the ground surface from the LIDAR signal. The detected ground points include bare surface pixels and locations where the laser was able to penetrate the vegetation canopy, but exclude buildings and vegetation. Buildings are then classified by detecting the extended low gradient regions on their roofs. The first return LIDAR data points are used to accurately detect building edges distorted by multi-path errors in the last return LIDAR data. The combined roof and edge surfaces are then employed to threshold the first and last return LIDAR height values and detect the LIDAR points reflecting from buildings. Once the building points are classified, the vegetation points are extracted from the remaining LIDAR points using a mask of the regions where there were significant differences in the first and last return of the laser pulse. The technique is robust for classifying LIDAR data acquired over a range of terrains with different vegetation cover and types and sizes of buildings. It requires minimal user intervention for parameter selection.