In aerial images, there are complex crisscross road lines; sometimes they are clear, sometimes they are not clear due to sunlight, aircraft jitter, or complex ground scenes. In the unclear cases, the image processing technology is required to enhance images for detecting roads as accurately as possible. For a low precision image, a road maybe a ridge or valley line (curve) with a certain width, whereas for a high precision image, in addition to the above properties, the solid and dashed lines in a road are also ridge edges. In most aerial images, there are woods, roads and railways, grass and crops, residential buildings, rivers, and mountains, etc., which lead to the complexity of ground scenes. Hence the roads are difficult to detect by a conventional image processing method. In most cases, for such a complex image, the first- and the second-order differential operators cannot accurately detect all roads, and the operators often extract only the high contrast objects and partial roads as shown in Fig. 1 (gray scale image) and Fig. 2 (color image), where, though the roads are ridge edges, some nonroad objects are similar to the roads. These features are clearly observed on the gray level histogram of the image section profile, and this is why traditional edge tracing algorithms can only detect partial roads that have obvious edges. So, a new ridge edge detection algorithm is studied in this study according to the above characteristics.