ABSTRACT
Remotely sensed data have been widely used in urban road extraction. Low resolution data cannot provide adequately detailed information for accurate estimation of road surface conditions. In low-resolution images, road extraction is often regarded as linear feature extraction because roads are considered to be “continuous and smooth lines” (Amini et al. 2002). Most traditional classication methods are based on statistical analysis of individual pixels. Those classiers are well suited to images with relatively low spatial resolution (Wang et al. 2004). As high-resolution satellite images become available, a large amount of detailed information of ground features may be readily available to extract. On high-resolution images, a road is no longer a linear network of a few pixels, but a ribbon of features with a certain width. Therefore, many factors, such as lanes, cars, pedestrians, and shadows of trees and buildings, may affect the extraction of roads from the satellite images.
