An automatic method for road extraction in rural and semi-urban areas starting from high resolution satellite imagery

In this paper an efficient method for automatic road extraction in rural and semi-urban areas is presented. This work seeks the GIS update starting from color images and using preexisting vectorial information. As input data only the RGB bands of a satellite or aerial color image of high resolution is required. The system includes four different modules: data preprocessing; binary segmentation based on three levels of texture statistical evaluation; automatic vectorization by means of skeletal extraction; and finally a module for system evaluation. In the first module the color image is rectified and geo-referenced. The second module uses a new technique, named Texture Progressive Analysis (TPA), in order to obtain the segmented binary image. The TPA technique is developed in the evidence theory framework, and it consists in fusing information streaming from three different sources for the image. In the third module the obtained binary image is vectorized using an algorithm based on skeleton extraction techniques and morphological methods. The result is an extracted road network which is defined as a structural set of elements geometrically and topologically corrects. The fourth module is an evaluation of the procedure using a popular method. Experimental results show that this method is efficient in extracting and defining road networks from high resolution satellite and aerial imagery.