The recognition of road network from high-resolution satellite remotely sensed data using image morphological characteristics

With the development of remote sensors and satellite technologies, high-resolution satellite data such as IKONOS images have been available recently. By these new high-resolution satellite data, remote sensing technologies can be successfully applied to more application areas such as extracting road network from high-resolution satellite images.. This paper proposes a newly developed approach to extract a road network from high-resolution satellite images. The approach is based on the binary and greyscale mathematical morphology and a line segment match method. First, the outline of road network is detected based on the grey morphological characteristics. Then, the basic road network is detected by the line segment match method. Next, the detected basic road network is processed based on the knowledge about the roads and binary mathematical morphological methods. Finally, visual analysis and three indicators are used to evaluate the accuracy of the extracted road networks. The results of the accuracy evaluation demonstrate that the developed road network extraction approach can provide both good visual effect and high positional accuracy.