Building classification using airborne lidar data with satellite SAR data

In general, airborne photogrammetry and LiDAR measurements are applied to geometrical data acquisition for automated map generation and revision. However, attribute data acquisition and classification depend on manual editing works including ground surveys. On the other hand, SAR data have a possibility to automate the attribute data acquisition and classification. Thus, we focus on an integration of LiDAR and SAR data to achieve a frequent map update with attribute data acquisition. In this study, we use airborne LiDAR and satellite SAR data to classify buildings. Firstly, we generate a digital surface model (DSM) from point cloud acquired with airborne LiDAR. Secondary, the DSM is registered with a normalized radar cross section (NRCS) image calculated from SAR data. Thirdly, buildings are extracted from the DSM. Finally, the buildings are classified into several clusters in the DSM. We clarified that a combination of airborne LiDAR and satellite SAR data can extract and classify buildings in urban area.