Urban land cover mapping using random forest combined with optical and SAR data

Accurate land covers classification is challenging in urban areas due to the diversity of urban land covers. This study presents a classification strategy with combined optical and Synthetic Aperture Radar (SAR) images using Random Forest (RF). Optimization of RF is conducted, indicating the optimal number of decision trees is 10 and the optimal number of features is 4 for splitting each tree node. The overall accuracy (OA) and Kappa coefficient are used to assess the classification. Result shows that classification with combined optical and SAR images (OA: 69.08%; Kappa: 0.6288) is higher than that with single optical image (OA: 81.43%; Kappa: 0.7770). Benefits of the combined use of optical and SAR images mainly come from reducing the confusions between water and shade, and between bare soil and dark impervious surfaces.