Abstract
This paper aims at the extraction of roads and road network from high-resolution dual-polarization synthetic aperture radar data over urban areas. According to the different features and applications for road network, the errors will be brought in the detection algorithm if it was not selected correctly. We proposed a modified extraction method making full use of available information to reduce such errors. In particular, we want to show how to implement road extraction algorithms based on the D-S evidence theory to establish the frame of discernment. The responses of two line detectors at the local analysis process were combined, which was done at the feature level by balancing the weight of two propositions constructed by responses of two line detectors. Then the road network optimization is accomplished using a Markov random field model of road, where both some contextual knowledge and global constraints were taken into account. The experimental results indicate that the proposed method is promising for main roads detection of urban areas.
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