Fusion Of Multispectral Image And Airborne Lidar Data For The Classification Of Urban Area With Rotation Forest

This study tested and compared the suitability of SPOT-5 image and LiDAR data both separately and combined for the classification of the urban area using Rotation Forest (ROF) classifier. Experimental results revealed that the integration of the SPOT-5 image and LiDAR data classification scheme gave better classification accuracies, when compared to the classification schedules using one of the two solely. Furthermore, RoF classifier produced better classification results than that of other two classifiers (i.e., SVMs (Support Vector Machines) and RF (Random Forests)). Finally, it should be noted that RoF classifier provided an effective way of combining SPOT-5 image and LiDAR data for classification, which is robust to the important parameter M (i.e. the number of features in each subset).