Road identification and refinement on multispectral imagery based on angular texture signature

The misclassification of roads and parking lots is one of the major difficulties in automating road network extraction from high resolution remotely-sensed imagery, especially in urban areas. This paper proposes a new integrated approach to road identification on high resolution multi-spectral imagery. The input images are first segmented using a traditional k-means clustering on normalized digital numbers. The road cluster is then automatically identified using a fuzzy logic classifier. A number of shape descriptors of angular texture signature are introduced for a road class refinement, i.e. to separate the roads from the buildings and parking lots that have been misclassified as roads. Intensive experiments have shown that the proposed methodology is effective in automating the separation of roads from buildings and parking lots on high resolution multi-spectral imagery.