Automatic Extraction of Road Network Based on the Integration of Sentinel-1 and Sentinel-2 Satellite Images with Texture Analysis Features in Non-urban Space (Case Study: Shahroud-Miami Route)

Remote sensing has wide applications in many scientific and research fields, including road engineering and transportation, the most important of which is extraction of road network and preparation of a schematic map of road network. Extraction of road network from satellite images is a complementary technology for accessing information, which simplifies the interpretation and analysis of image and improves the quality, which is one of the most important goals of planners. The main purpose of this study was to automatically extract road network on Shahroud-Miami route so that the resulting road network map can be used as the input of pavement management system (PMS). The proposed method in this study is based on the technique of merging and combining images of Sentinel-1 and Sentinel-2 satellites with the majority voting method in order to make maximum use of spectral and spatial information of multiple images (detail increase) instead of single image using texture features. Then, for the monitored classification, three classifications of artificial neural network (ANN), support vector machine (SVM) and maximum likelihood of similarity (ML) were used in two general classes of road and non-road. Random and homogeneous experimental samples and evaluation samples from the existing images and maps of the region were used to assess the accuracy of classification. Results of this study showed that integration of the results of classifications with the majority voting method improved the accuracy by 4% for Sentinel-1 satellite and 6% for Sentinel-2 satellite in identifying the route and the road network. Also, the kappa coefficient in the majority voting method has increased by about 0.11 for Sentinel-1 satellite and by about 0.06, compared to ANN (the best effective classification performance).