<title>Automatic extraction of road networks from remotely sensed images based on GIS knowledge</title>

Abstract

Automatic extracting and updating road networks is a key work for updating geo-spatial information especially in developing countries. In this paper, a whole framework for automatic road extraction is presented firstly. Then the strategy and algorithms using GIS data for road extraction are discussed. A hybrid method based on structure information and statistical information for road extraction is emphasized in this paper. Different extraction strategy and grouping techniques are employed for different extracting methods. Because of the importance of structure information in road extraction, the extraction of candidate road segments based on structure information is described. For road extraction from images with different resolution based on structure information, different grouping technique is applied. The grouping technique based on whole relation and the grouping technique based on new profile tracing algorithm is separately employed for images with low resolution and with high resolution. The road extraction based on statistical information is the supplement of structure information. A new statistical model is presented and the candidate road-tracing algorithm based on adaptive template is discussed. And the grouping based on ribbon-snake model is briefly introduced. Automatic road recognition is a necessary task for automatic extracting road networks. So aiming at this we put all kinds of road recognition knowledge into the knowledge base and build a road recognition expert system. The fuzzy theory is applied for representing road models and road knowledge reasoning. The strategy for using global information to guide the further road extraction is presented. At last some examples and the summary are given.© (2003) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only.