Urban land use extraction from Very High Resolution remote sensing imagery using a Bayesian network

Urban land use extraction from Very High Resolution (VHR) remote sensing images is important in many applications. This study explores a novel way to characterize the spatial arrangement of land cover features, and to integrate it with commonly used land use indicators. Characterization is done based upon building objects, taking their functional properties into account. We categorize the objects to a set of building types according to their geometrical, morphological, and contextual attributes. The spatial arrangement is characterized by quantifying the distribution of building types within a land use unit. Moreover, a set of existing land use indicators primarily based upon the coverage ratio and density of land cover features is investigated. A Bayesian network integrates the spatial arrangement and land use indicators, by which the urban land use is inferred. We applied urban land use extraction to a Pléiades VHR image over the city of Wuhan, China. Our results showed that integrating the spatial arrangement significantly improved the accuracy of urban land use extraction as compared with using land use indicators alone. Moreover, the Bayesian network method produced results comparable to other commonly used classifiers. We concluded that the proposed characterization of spatial arrangement and Bayesian network integration was effective for urban land use extraction from VHR images.