Abstract
Abstract. High quality and updated road network maps provide important information for many domains. Many small segments appear on the road surface in VHR images. Most road extraction systems have problem in extraction of these small segments and usually they appear as gaps in the final extracted road networks. However, most approaches skip filling these gaps. This is on account of the fact that usually overall length of the missing parts of the road extraction results is very short relative to the total length of the whole road network. This leads to an indiscernible impact of filling these gaps on geometrical quality criteria. In this paper, using two different VHR satellite datasets and a gap-filling approach which is based on tensor voting, we show that utilizing an effective road gap filling can result in a quite tangible topological improvement in the final road network which is highly demanded in many applications.
Highlights
Due to the importance of updated road network database, automated road extraction is an important study area in remote sensing
This improvement, especially when the computational load of the tensor voting algorithm is considered into account, may not be convincing for embedding gap filling step to road extraction systems
Analyzing the topological quality of the extracted road networks shows that existence of gaps in a road network, even with a high geometric quality, may lead to a low level of topological quality
Summary
Due to the importance of updated road network database, automated road extraction is an important study area in remote sensing. Most road extraction systems skip this step This is on account of the fact that usually the length of the missing parts i.e., gaps is very short relative to the total length of the network, which leads to an indiscernible impact of filling these gaps on geometrical quality criteria such as completeness, correctness and quality. This inherent functionality of the roads which is a unique property w.r.t other objects like buildings and vegetation should be considered in road extraction/updating systems. This property is considered as connectedness in (Wegner et al, 2015) and topological quality of the extraction results is investigated, thoroughly. Different approaches for road extraction from various types of images are developed and geometrical quality of the results are evaluated. The effect of gap filling on the quality of the extracted network is not analyzed yet which is the main focus of this paper
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