Evolving roads in IKONOS multispectral imagery

The introduction of satellite imagery characterized by high spectral and spatial resolutions has made possible the development of new viable approaches for the accurate, and cost-effective extraction of linear features with minimal human intervention. This paper presents a semi-automated method for the extraction of roads from (1-meter) pan-sharpened multispectral IKONOS imagery. An operator provides an initial seed point on the road of interest, then the region is evolved using a level set method. Further analysis through iterative smoothing refines the extracted region to accurately estimate the road centerline despite the presence of cars on the road, changes in the pavement or surface properties of the road, or obstruction resulting from foliage or shadows cast on the road by neighboring trees. Initial results have demonstrated the utility of the algorithm in efficiently extracting roads from high resolution satellite imagery with minimal human interaction. Over 97% delineation accuracy was achieved on manually ground truthed IKONOS image samples overlooking both urban and rural locations.