Abstract
The digital road map is an important basis for the Intelligent Transport System and Geographic Information System; however, almost all digital maps are currently generated manually from paper maps. As it is very time consuming to generate digital road maps this way, we propose a system to generate digital maps using image processing of aerial images. Conventionally, most methods of road extraction use RGB features. However, the use in urban areas of RGB features has many problems as they are affected severely by shadows cast by buildings. On the other hand, road extraction using a Digital Surface Model (DSM) is not affected by shadows because a DSM is elevation data. However, it is too difficult to distinguish between a road and other objects, such as a railway, because the elevations of these objects are similar. In this paper, we use DSM and RGB aerial images to resolve the problems of each type of data. We segment a DSM to extract the road areas from the DSM; however, the segmentation method conventionally used could not extract narrow roads because that method is sensitive to noise in a DSM. In light of this, we next propose a method of image segmentation using Boundary code. The proposed segmentation method is robust against the noise in a DSM. In an experiment using actual data, the accuracy of road extraction using the proposed method was 83.7%, against the 76.0% achieved using the conventional method. In addition, the proposed method achieves faster segmentation than the conventional method. Moreover, this method extracted road areas more accurately in urban areas than by just using RGB or DSM.
