Abstract
Road networks are very important features in geospatial databases. Even though high-resolution optical satellite images have already been acquired for more than a decade, tools for automated extraction of road networks from these images are still rare. One consequence of this is the need for manual interaction which, in turn, is time and cost intensive. In this paper, a multi-stage approach is proposed which integrates structural, spectral, textural, as well as contextual information of objects to extract road networks from very high resolution satellite images. Highlights of the approach are a novel linearity index employed for the discrimination of elongated road segments from other objects and customized tensor voting which is utilized to fill missing parts of the network. Experiments are carried out with different datasets. Comparison of the achieved results with the results of seven state-of-the-art methods demonstrated the efficiency of the proposed approach.
Highlights
Automated extraction of man-made objects from aerial and satellite imagery has been explored for decades
Starting points for further research arose over time and have been influenced by many factors, such as the availability of very high resolution (VHR) optical satellite imagery
To addressindex the first characteristic, we propose a novel linearity index called the skeleton-based object linearity (SOLI)
Summary
Automated extraction of man-made objects from aerial and satellite imagery has been explored for decades. Due to the variety of methods in the literature, a strict categorization of all approaches dealing with road extraction methods is a difficult task [7]. For the sake of conciseness, we confine our literature review to the recent state-of-the-art approaches in road extraction from VHR images. For a detailed literature review on road extraction researches, which have been carried out before 2003, the reader is referred to Mena et al [7]. In 2006, Mayer et al [8] compiled a EuroSDR test and compared results of different approaches for automatic road extraction from VHR satellite and aerial images
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