An Approach of Semiautomated Road Extraction from Aerial Image Based on Template Matching and Neural Network

In this paper, we propose a semiautomatic road extraction scheme that is based on template matching and optimization by Hopfield neural network. In the semiautomatic way, a road is extracted automatically after a series seed points have been given coarsely by the operator through a convenient interactive image-graphics interface. Attending to accuracy, robustness, speed and interactivity, we use a binary profile template as the local gray model to speed up the template matching and build a Hopfield neural network to select the ‘best road way’ form the candidates gotten from template matching. The template is generated by ‘darkness-brightness-darkness’ local road feature so it is mainly aim at extraction of ‘light ribbon like road’. The Hopfield model is built according to the geometric and gray constraint of road on aerial image. Even there is serious noise, the algorithm extracts road well. The algorithm can extract the road of which width is from a few pixels to more than 100 pixels. This paper describes the principle and steps of the approach. Some experimental results and discussions about semiautomatic road extraction are also given.