LAUSR

Urban scene classifications with very-high-resolution (VHR) satellite images play an important role in urban functional-zone investigation and landscape analysis. Most scene classification techniques utilize visual features which however are weak in identifying scenes, as scenes are usually composed of diverse objects with variant visual cues. To resolve this issue, we use spatial object relations to characterize urban scenes and recognize their categories. Accordingly, two questions are considered in this study: how to measure spatial object relations and how to use these relations to classify urban scenes? First, we propose a novel scene feature, semantic and spatial co-occurrence probability (SSCP), to measure spatial relations between objects with considering their directions, distances, and semantics, which can hopefully resolve the three key issues in measuring spatial object relations, i.e., anisotropy, scale- and semantic-dependencies. Then, a semilatent Dirichlet allocation is employed to classify scenes based on the proposed SSCP. In experiments, our method is first verified based on the UC Merced data set, and then used to generate an urban functional-zone map for Beijing, China. Both experiments indicate high accuracies and state-of-the-art performances of our method. In addition, we give a deep insight into SSCP's characteristics including convergence, high efficiency, and invariance to affine transformation, which can make our method more applicable.