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AUTOMATIC ROAD EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGES USING NEURAL NETWORKS , TEXTURE ANALYSIS , FUZZY CLUSTERING AND GENETIC ALGORITHMS

In this article, a new method for road extraction from high resolution Quick Bird and IKONOS pan-sharpened satellite images is presented. The proposed methodology consists of two separate stages of road detection and road vectorization. Neural networks are applied on high resolution IKONOS and Quick-Bird images for road detection. This paper has endeavoured to optimize neural networks’ functionality, using a variety of texture parameters. These texture parameters had different window sizes and grey level numbers, not only from source but also from pre-classified image. Road vectorization is based on the idea of road raster map clustering obtained from the previous road detection stage. In this step, despite of genetically guided clustering, a new flexible clustering methodology is proposed for road key point identification. The last step of road key point connecting is carried out based on the obtained information from a fuzzy shell based clustering. The accuracy assessment of the obtained vectorized road network proved the ability of the proposed method in sub-pixel road extraction. * Corresponding author