Automated Extraction of Buildings and Roads in a Graph Partitioning Framework
This paper presents an original unsupervised framework to identify regions belonging to buildings and roads from monocular very high resolution (VHR) satellite images. The proposed framework consists of three main stages. In the first stage, we extract information only related to building regions using shadow evidence and probabilistic fuzzy landscapes. Firstly, the shadow areas cast by building objects are detected and the directional spatial relationship between buildings and their shadows is modelled with the knowledge of illumination direction. Thereafter, each shadow region is handled separately and initial building regions are identified by iterative graph-cuts designed in a two-label partitioning. The second stage of the framework automatically classifies the image into four classes: building, shadow, vegetation, and others. In this step, the previously labelled building regions as well as the shadow and vegetation areas are involved in a four-label graph optimization performed in the entire image domain to achieve the unsupervised classification result. The final stage aims to extend this classification to five classes in which the class road is involved. For that purpose, we extract the regions that might belong to road segments and utilize that information in a final graph optimization. This final stage eventually characterizes the regions belonging to buildings and roads. Experiments performed on seven test images selected from GeoEye-1 VHR datasets show that the presented approach has ability to extract the regions belonging to buildings and roads in a single graph theory framework.