Road intersection recognition based on a multi-level fusion of vehicle trajectory and remote sensing image

Road intersections are important components of a road network, which are not only numerous and diverse in shape, but also complex in structure and different in size. It is difficult to recognize comprehensive and accurate road junctions based on single data source, as its limited describe information. To this end, this paper designs a multiple integration method to identify road intersections from vehicle trajectories and remote sensing images. Firstly, based on the unsupervised idea, a method combining morphological processing, density peak clustering and tensor voting is proposed to extract the seed intersections, which is regarded as a small sample set. Based on it two intersection classifiers based on deep convolution network and oriented to vehicle trajectories and remote sensing images are constructed by using collaborative training mechanism, and finally, the advantages of the two models are combined to form an integrated classification model of road intersections. In this paper, a semi supervised intersection extraction technology is proposed by fusing the complementary description features of vehicle trajectories and remote sensing images on multiple levels, which can effectively identify complex and diverse road intersections without manual labeling. Experiments based on Wuhan taxi trajectories and remote sensing images show that the accuracy of this method is more than 93% and the recall rate is 87% without manually labeled samples.