期刊名称：ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
印刷版ISSN：2194-9042
电子版ISSN：2194-9050
出版年度：2008
卷号：XXXVII Part B3b
页码：535-538
出版社：Copernicus Publications
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 are 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
