A novel approach based on the combination image of fraction image and normalized mnf image to urban land use/cover mapping

Urban land use/cover mapping is very important and it is the base and foundation of further urban analysis and research. Whereas urban land use/cover mapping of using medium spatial resolution remotely sensed images presents numerous challenges due to the intensive heterogeneity of urban landscapes. In order to solve the above challenges and improve the accuracy of urban land cover/use mapping, we proposed a novel approach, which produced firstly combination image based on fraction image and normalized MNF image and then performed decision tree classification and gained urban land use/cover mapping at last. An ETM+ image was used as data source and Nanjing City, China was selected as study area. The accuracy of classification result was validated using IKONOS images and was compared with the other three classification schemes. Results show that this decision tree classification based on the combination image is superior to the other classification schemes.