Automatic Road Detection in Grayscale Aerial Images

Abstract

Digital aerial photography provides a useful starting point for computerized map generation. Features of interest can be extracted using a variety of image-processing techniques, which analyze the image for characteristics such as edges, texture, shape, and color. In this work, we develop an automatic road detection system for use on high-resolution grayscale aerial images. Road edges are extracted using a variant of the Nevatia-Babu edge detector. This is followed by an edge-thinning process and a new edge-linking algorithm that fills gaps in the extracted edge map. By using a zoned search technique, we are able to design an improved edge-linking algorithm that is capable of closing both large gaps in long, low-curvature road edges and smaller gaps that can occur at triple points or intersection points. An edge-pairing algorithm, which is subsequently applied, takes advantage of the parallel edges of roads to locate the road centers. Results demonstrate that the current system provides a simple yet effective first stage for a more sophisticated map-generation system.