Research on Classification of Land Cover based on LiDAR Cloud and Aerial Images

Aerial images contain abundant spectral information，texture information and spatial information，and airborne LiDAR can provide three-dimensional information of ground objects.An object-oriented classification method was researched by taking advantages of the two types of data.Converting LiDAR point cloud into 2-D raster image by preprocessing，and matched it with aerial image.Then，multi-scale segmentation algorithm was applied to image segmentation based on spectral information and height information.Next，XGBoost algorithm were applied to select features extracted from segmented object respectively.The SVM classifier was used to classify and prove the superiority of XGBoost algorithm by comparing with two traditional feature selection algorithms：Relief and RFE.Finally，objects at shadow regions were distinguished and merged into real objects based on certain rules.Testing the method in three regions，the results showed that the method was feasible and effective，and could be well applied to the classification of urban ground object.