Road network extraction from sar imagery supported by context information

This paper deals with automatic road extraction from SAR imagery. In general, automatically extracted road networks are not complete, i.e., gaps remain in the erxtracted network. Especially in SAR imagery many objects occlude road sections and cause gaps, due to the side looking geometry of the SAR sensor. In this paper an approach for automatic road extraction is proposed that is optimized for rural areas by using additional explicitly modeled knowledge about roads and the context of roads. Roads are modeled as a network. Context of roads is hierarchically structured into a global and a local level. Local context objects like trees or vehicles can interfere road extraction due to the layover effect or the motion, but they can also support it. It is shown that the incorporation of local context objects into the extraction improves the results by bridging smaller gaps. Though the approach is restricted to rural areas, other global context regions can provide additional information, too. Here, urban areas are used to deliver confident seed information for the road network generation, because it is the characteristic and function of roads to connect urban areas with each other. With this information a more complete network is extracted. Furthermore, a new approach for highway extraction is proposed based on a multi-scale modeling. Because of the larger dimensions of highways and the more salient substructures, like the crash barriers, a more detailed model and extraction strategy is needed. Finally, examples and results are given, showing the potential of using context information and explicit modeling of roads for automatic road extraction from SAR imagery.