Rule-based Classification of Airborne Laser Scanner Data for Automatic Extraction of 3D Objects in the Urban Area

Abstract

LiDAR technology has been widely adopted as a proper method for land cover classification.Recently with the development of technology, LiDAR systems can now capture high-resolutionmultispectral bands images with high-density LiDAR point cloud simultaneously. Therefore, it opens newopportunities for more precise automatic land-use classification methods by utilizing LiDAR data. Thisarticle introduces a combining technique of point cloud classification algorithms. The algorithms includeground detection, building detection, and close point classification - the classification is based on pointclouds’ attributes. The main attributes are heigh, intensity, and NDVI index calculated from 4 bands ofcolors extracted from multispectral images for each point. Data of the Leica City Mapper LiDAR systemin an area of 80 ha in Quang Xuong town, Thanh Hoa province, Vietnam was used to deploy theclassification. The data is classified into eight different types of land use consist of asphalt road, otherground, low vegetation, medium vegetation, high vegetation, building, water, and other objects. Theclassification workflow was implemented in the TerraSolid suite, with the result of the automation processcame out with 97% overall accuracy of classification points. The