Road Network Extraction in VHR SAR Images of Urban and Suburban Areas by Means of Class-Aided Feature-Level Fusion
ABSTRACT In this paper, we propose to combine two road extractors from very high resolution synthetic aperture radar scenes: one more successful in rural areas and one explicitly designed for urban areas. In order to get the best combination of both, a rapid mapping filter for discriminating rural and urban scenes is utilized. Finally, the results are fused on a feature level and connected by means of a network optimization. The approach is tested and evaluated on TerraSAR-X data containing complex urban areas and urban-rural fringe scenes.
Conference Proceeding: Road extraction using K-Means clustering and morphological operations[show abstract] [hide abstract]
ABSTRACT: In this paper we proposed the method for road extraction. The road extraction involves the two main steps: the detection of road that might have the other non road parts like buildings and parking lots followed by morphological operations to remove the non road parts based on their features. We used the K-Means clustering to detect the road area and may be some non road area. Morphological operations are used to remove the non road area based on the assumptions that road regions are an elongated area that has largest connected component.Image Information Processing (ICIIP), 2011 International Conference on; 11/2011