A Linear Dirichlet Mixture Model for decomposing scenes: Application to analyzing urban functional zonings

Abstract Urban scenes are fundamental to assessing urban landscapes and analyzing the spatial arrangements of functional zonings. Thus, it is important to obtain the information on urban scenes automatically from high-resolution remote sensing images. Previous studies have focused solely on obtaining qualitative labels of scenes (i.e., scene classification) as opposed to quantitative measurements on scenes (i.e., scene decomposition). However, scene classification alone cannot satisfy the demands of practical applications, as most scenes are mixed and composed of many categories. This study aims at 1) developing a decomposition model for quantifying mixed semantics of urban scenes, and 2) applying the proposed model to analyze urban functional zonings. For the first target, a Linear Dirichlet Mixture Model (LDMM) is proposed to quantify the relationships between pure scenes (including one single category) and mixed ones (including multiple categories). Since both mixed and pure scenes are characterized by frequency features (e.g., gray histograms of shapes), the variations in the quantization levels of frequency features could lead to different decomposition results. Accordingly, the stationary Markov process is further introduced to estimate the convergent decomposition results. For the second target, the LDMM was applied to obtain the qualitative categories and their quantitative proportions of urban scenes in Beijing and Zhuhai and to assess their spatial arrangements and temporal changes. Our methods were experimentally verified to be reliable and more effective in performance than the Linear Mixture Model and scene classification techniques. The case studies also suggested that Beijing and Zhuhai significantly differ in urban functional zonings and the functional zonings in Beijing have changed much over time, indicating an important transition in the city's economic structure.