Fusion of LiDAR and radar data for land-cover mapping in natural environments

Land-cover geodatabases are key products for the understanding of environmental systems and for setting up national and international prevention and protection policies. However, their automatic generation and update remain complicated with high accuracy over large scales. In natural environments, most of the existing solutions are semi-automatic in order to achieve a suitable discrimation of the large number of forest and crop classes. A large amount of remote sensing possibilities is at the moment available and data fusion appears to be the most suitable solution for that purpose. The paper tackles the issue of land-cover mapping in such areas assuming the existence of a partly non-updated 5-class geodatabase: buildings, roads, water, crops, forests. Lidar point clouds and Radar images at two spatial resolutions and bands are merged at the feature level and fed into an efficient supervised classification framework. Results show that some classes benefit from the joint exploitation of multiple observations in terms of accuracy or recall.