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A method for extracting urban road with shadow from the Chinese Gaofen-2 data

With the improvement of remote sensing satellite spatial resolution, more recognizable details can be obtained from remote sensing image. It may lead to inaccurate classification results. Most existing methods cannot correctly discriminate roads from its background, especially when the road is in complex scenes. To improve the accuracy of occluded road extraction, the spectral characteristics of shadow areas and roads in different bands are calculated. There are significant differences among land covers in the near-infrared band. Therefore, the near-infrared image of Gaofen-2 (GF-2) is selected for pre-processing to extract the urban road in this paper. First, the road feature is more prominent and similar to shaded region in the near-infrared image. Further setting threshold segmentation to extract road can reduce fracture caused by occlusion. Next, the centerline will be extracted after removing misclassified parts. The unwanted regions can be divided into two main types. One is the isolated non-road patch, which can be removed using the geometric features of road. The other is the connected false segment, which can be removed in conjunction with the width and medial axis skeleton of road. At last, the metric function and neighborhood similarity are employed to link broken centerlines to generate a complete road network. The qualitative and quantitative experimental results demonstrate that our method can better perform urban road extraction with shadow and the extracted centerline is smoother.