An Effective Adjustment on Improving the Process of Road Detection on Raster Map

Abstract

This research goal is to improve whole process of recognizing and extracting road layer from raster map, and to make the whole process as a closed feedback system with output measurable, state controllable and recognition sub-process optimally adaptable. The scheme is building an integrated system with its output road layer measurable by selecting two (or more) methods of perfect comparatively identifications with their mechanism complementary and then parallel collecting them as the controlled sub-process objects of the integrated system. Based on approach thought of microscopic identification and macroscopic elimination, the system realized first the road layer initial recognition and last road map level completely to withdraw via some output layers re-clustering and feedback control. In view of the standard municipal transportation map, the path and the region valve value condition has determined. Through re-clustering to the noise the whole map can be completely divided into two parts of road and non-road (region). The feedback re-clustering strategies of recognition for the transportation road networks are based on the core characteristic of obtain road constitution. The convergence of the feedback re-clustering of road layer guarantees the map level optimization

^* This work was supported in part by the National Natural Science Foundation of China under Grant 60974092.