A New Approach to 3D Dense LiDAR Data Classification in Urban Environment

Abstract

Classification of Mobile Mapping LiDAR (Light Detection and Ranging) data is a challenge in the research community since the day when laser scanner system were integrated and mounted on vehicles for collection of 3D data in urban environment. The approach proposed here for classifying LiDAR data is analogous to the process followed for classifying data from satellite images. Pixel based and segmentation based methods have been employed in past for classifying images obtained from satellites. These methods were based on spectral properties of objects present in the images. But for Mobile mapping LiDAR data this approach has been applied and tested for the first time. The properties of this data are completely different from that of satellite images. So even if the basic approach remains the same, many changes have to be made in the entire classification process. The paper here aims to propose the basic procedure of using pixel-wise classification on dense 3D LiDAR data.