Modeling urban structures using graph-based spatial patterns

We introduce a new method for modeling the spatial arrangements of geospatial objects. As opposed to the existing approaches that are based on classifying images using pixel level methods, we propose to use objects as textural primitives and exploit their spatial patterns. First, the primitives are detected using spectral and morphological processing. Then, these primitives form the nodes of a graph where the neighborhood information is obtained through Voronoi tessellation of the image scene. Next, this graph is clustered by thresholding its minimum spanning tree. Finally, the resulting clusters are classified as regular or irregular by examining the distributions of the angles between neighboring nodes. Experiments using Ikonos images show that the application of the proposed model where buildings are used as the primitives and building groups are automatically classified as organized or unorganized can extract valuable information about urban development.