URBAN ROAD EXTRACTION VIA GRAPH CUTS BASED PROBABILITY PROPAGATION

In this paper, we propose a graph cuts (GC) based probability propagation approach to automatically extract road network from complex remote sensing images. First, the support vector machine (SVM) classifier with a sigmoid model is applied to assign each pixel a posterior probability of being labelled as road class, which avoids the weaknesses of hard labels in general SVM. Then a GC based probability propagation algorithm is employed to keep the extracted road results smooth and coherent, which can reduce the connections between roads and road-like objects. Finally, a road-geometrical prior is considered to refine the extraction result, so that the non-road objects in images can be removed. Experimental results on two remote sensing image datasets indicate the validity and effectiveness of our method by comparing with two other approaches.