Object-based classification using LiDAR-derived metrics and QuickBird imagery

Abstract

Due to the strengths and weaknesses of the airborne LIDAR data and QuickBird multispectral data, an improved classification method is presented for extracting vegetation information, roads, and buildings. A plot located in San Francisco was selected as the study site. Firstly, ground points were extracted from the LIDAR data and resampled to build DEM and DSM, and then derived nDSM by subtracting DEM from DSM. Secondly, the intensity information derived from LiDAR data was processed to be distributed evenly, and then generated an intensity clustering image, which classified LiDAR points into two basic clusters. Finally, add nDSM and intensity clustering images to QuickBird image as two extra bands, and then we can extract vegetation information, roads, and buildings using their height, intensity and spectral information. The results showed that the method combined airborne LIDAR-derived metrics and QuickBird multispectral data has higher classification accuracy. The proposed method in the paper could be applied to larger research area and other fields.