# Progressive Dynamic Registration Method for Tile Maps Based on Optimum Multi-Features
## 5 Discussion  
In the six scenarios (remote sensing maps of big city, remote sensing maps of small city, remote sensing maps of nonurban areas, vector maps of big city, vector maps of small city, and vector maps of nonurban areas), the average RMSE of the PDR method is lower than that of the SURF + RANSAC method, especially in vector map. Furthermore, the PDR method is more stable. This is because the image difference between the two vector maps is obvious, and it is difficult to match enough corresponding point pairs according to the descriptor information of the feature points. On the one hand, we propose a feature point filtering strategy for the characteristics of tile maps to improve the match success rate. On the other hand, when a certain proportion of correct matching feature point pairs cannot be obtained, feature surfaces are used for registration. Therefore, the PDR method performs better.  
Compared with the method using coordinate system conversion, the registration effect of the PDR method is better in remote sensing maps and vector maps in nonurban areas and is poorer in vector maps of some urban areas. The remote sensing maps of different manufacturers are different images of the same features, and there are many corresponding points between images. Using the PDR method for registration, a large number of correct matching point pairs can be obtained stably, and the calculation results are accurate. In contrast, the remote sensing maps of map manufacturers are made from many remote sensing images stitched and encrypted in bulk using various methods, and the accuracy is low. Therefore, better registration results can be achieved in both urban and nonurban areas. However, the drawing style of vector maps varies greatly among different manufacturers, and the measurement coordinates of the same element may be different. It is difficult for the registration method based on image features to achieve stable registration results. In the vector map of urban areas, when the map scale is small, urban arterials with accurate coordinates are the main part of the map, and there are fewer text and road signs with inaccurate coordinates in the map, so stable feature point pairs can be extracted, and the RMSE of the PDR method is low. As the scale increases, there are fewer urban arterials and more minor roads and boundaries of the community, which is unreliable; it is difficult to obtain enough feature point pairs after using the algorithm filtering, and then feature surfaces are used for registration. The inaccurate polygons of minor roads and community boundaries in urban areas make the extraction of polygons difficult, and the RMSE is high. In large cities, the road system is more developed, and the coordinates are more accurate, so more feature points are extracted, fewer scenes are registered using feature surfaces, and the RMSE of the PDR method is lower than in small cities. In the experiments, feature surfaces are used for registration from zoom 16.5 in Beijing, while it is from zoom 14 in Ganzhou. In the vector map of non-urban areas, there are fewer geographic elements, and it is easy to extract the corresponding geographic feature surfaces, and the registration effect is better.