Automatic Road Extraction from Irs Satellite Images in Agricultural and Desert Areas

The appearance of roads in northern Africa differs from that of roads, e.g., in central Europe, which most of the approaches for automated road extraction in literature focus on. In this paper we propose a road model for areas with different road appearance in IRS satellite image data with a panchromatic resolution of 5 m and 20 m multispectral resolution. We model areas where water makes agriculture possible on one hand, and areas dominated by the desert and dry mountainous areas on the other hand. In the desert and mountainous areas paved roads appear as more or less distinct lines and the Steger line extraction algorithm can be used to extract roads in combination with global grouping. In mountainous areas detected, e.g., in a DEM, much larger curvatures are expected to occur than in the desert. In agricultural areas, on which we focus in this paper, roads often do not appear as distinct lines. Borders of the fields represented by edges in the image and the knowledge that these borders can be collinearly grouped, possibly together with lines, into longer linear structures are used to construct road sections. To close gaps, pairs of lines or edges are connected by ziplock snakes. To verify these road sections, the paths of the snakes are evaluated using the line strength and the gradient image. The verified road sections are finally globally grouped using the knowledge that roads construct a network between important points. Gaps which have a high impact on the network topology are closed if evidence supporting this is found in the image. Results show the validity of the approach.