RESEARCH ON ROAD EXTRACTION SEMI-AUTOMATICALLY FROM HIGH RESOLUTION REMOTE SENSING IMAGES

摘要

Nowadays, there have substantive research on road extraction automatically form RS images, restricted by low understanding level of images, the automatically extracting method is not robust, and a great many errors existed. The LSB-Snake model is an effective method to extract linear object semi-automatically, but needs manual input of road character for extraction, and not robust while the initial seed points arc not dense enough. These hold down the working efficiency of LSB-Snake model. This paper put forward an auto-initial-valued LSB-Snake model, which use self-adapt template matching method to provide the road character to LSB-Snake model, and add seed points based on the initial points at the same time automatically. Experiments indicate: Given the same amount of initial seed points, our method is more robust than LSB-Snake model; Needn't manual input the road character, the auto-initial-value LSB-Snake model is more automatic than LSB-Snake model; The auto-initial-value LSB-Snake model can overcome the shade or shelter of land objects such as building and trees, and more powerful in anti-jamming than LSB-Snake model. The methods this paper put forward can extract road feature from remote sensing image efficiently.