| dc.description.abstract | Road network is one important index for urbanization. The information of roads provides various applications in daily life, such as urban design, navigation, and urban mapping. With the development of the city, transportation becomes more convenient and road networks also change frequently. Also after typhoons, heavy rains or earthquakes, there are usually some debris flows which may block the roads in mountainous area. Because the update of this information manually is tedious and time-consuming, for the purpose of road network updates and transportation management after disasters, automatic road extraction from optical remotely sensed images becomes an economic and efficient approach to obtain and update road networks.
In the past few decades, many approaches are proposed to extract road from remote sensing imagery, but most of studies have applied on the road extraction from low-resolution imagery. Because of the complexity of road characteristics, road extraction in high resolution images is quite different from in low resolution images. Therefore, the study of the urban road network extraction has important theoretical and practical significance.
In this study the urban road extraction of the high resolution remote sensing images based on the several basic characteristics of roads. Our proposed method includes the following steps. First, the multi-scale retinex method is used to enhance the image, and then the k-means algorithm is used to obtain the initial outline of roads. Followed by the road’s homogeneous property, the illuminant invariance theory and the multi-weighted method are used to improve the accuracy of outline. Then morphology is adopted to eliminate noise and short lines. Finally the shape index is used to remove non-road areas .
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