Classification of Multispectral Airborne LiDAR Data Using Geometric and Radiometric Information

Abstract

Classification of airborne light detection and ranging (LiDAR) point cloud is still challenging due to the irregular point cloud distribution, relatively low point density, and the complex urban scenes being observed. The availability of multispectral LiDAR systems allows for acquiring data at different wavelengths with a variety of spectral information from land objects. In this research, a rule-based point classification method of three levels for multispectral airborne LiDAR data covering urban areas is presented. The first level includes ground filtering, which attempts to distinguish aboveground from ground points. The second level aims to divide the aboveground and ground points into buildings, trees, roads, or grass using three spectral indices, namely normalized difference feature indices (NDFIs). A multivariate Gaussian decomposition is then used to divide the NDFIs’ histograms into the aforementioned four classes. The third level aims to label more classes based on their spectral information such as power lines, types of trees, and swimming pools. Two data subsets were tested, which represent different complexity of urban scenes in Oshawa, Ontario, Canada. It is shown that the proposed method achieved an overall accuracy up to 93%, which is increased to over 98% by considering the spatial coherence of the point cloud.