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).

In this letter, multisource earth observation (EO) data sets, including multitemporal Landsat-8, digital surface model, and spatial information, were integrated for land-cover classification by random forest (RF) and support vector machines (SVMs). We demonstrated in this letter that both RF and SVM are useful tools for classification of land cover in the local climate zones featured with highly heterogeneous landscape. Classification of land cover by RF was with an overall accuracy (OA) of 86.2%, while the OA was 85.5% for SVM. However, we found that RF was more stable than SVM for multisource EO data in classifying land cover without normalizing different feature data sets. Experiments showed that the thermal features were more important than temporal and spatial ones in discriminating impervious objects, while the temporal and spatial features were generally better than thermal ones in separating the distinct vegetation categories. Another finding was that our experiments indicated that spectral features were the most important in classification of land cover, followed by temporal, thermal, and spatial features, respectively. As to the spectral features, red channels were the most important, followed by short-wave infrared, near-infrared, and green channels. Thus, it could be concluded that the combination of spectral, thermal, spatial, and temporal information would be an optimal approach to increase the OA of land-cover classification in the zones featured with highly heterogeneous landscape.