Automatic Extraction of Road Networks in Urban Areas from Ikonos Imagery Based on Spatial Reasoning

In this study we developed a spatial reasoning-based method of automatically extracting roads in a densely populated suburb of Auckland, New Zealand from IKONOS data. First, all of the four multispectral bands were grouped into 20 clusters in an unsupervised classification, two of which corresponded to road networks. This intermediate result was then converted into a binary image of road and non-road pixels. This binary image was then further processed with spatial reasoning in two ways. First, all isolated or small clusters of pixels were examined spatially to determine if there were other isolated pixels in their immediate vicinity. If no neighbouring pixels were found, they were considered as noise and removed from the image. If neighbouring pixels were found, their position in relation to the pixel under consideration was further analyzed. If they were aligned with existing pixels along a certain orientation, then they were regarded as a portion of a disjoined road and retained in the output image. Seconds, these disjoined road segments were later joined together to form a road network. The extracted road network was unified to a constant width because trees planted along both sides of a road caused its width to vary in different sections. The detected results using a threshold of six pixels show that most roads can be extracted at a reasonable accuracy level.