Abstract
With the rapid development of intelligent transportation, there comes huge demands for high-precision road network maps. However, due to the complex road spectral performance, it is very challenging to extract road networks with complete topologies. Based on the topological networks produced by previous road extraction methods, in this paper, we propose a Multi-conditional Generative Adversarial Network (McGAN) to obtain complete road networks by refining the imperfect road topology. The proposed McGAN, which is composed of two discriminators and a generator, takes both original remote sensing image and the initial road network produced by existing road extraction methods as input. The first discriminator employs the original spectral information to instruct the reconstruction, and the other discriminator aims to refine the road network topology. Such a structure makes the generator capable of receiving both spectral and topological information of the road region, thus producing more complete road networks compared with the initial road network. Three different datasets were used to compare McGan with several recent approaches, which showed that the proposed method significantly improved the precision and recall of the road networks, and also worked well for those road regions where previous methods could hardly obtain complete structures.
Highlights
Road topology reconstruction is a fundamental yet long-standing problem for remote sensing applications [1,2,3], receiving wide attention in the past decades
We show the performance of Multi-conditional Generative Adversarial Network (McGAN) on the three datasets (Section 4.3)
This paper proposes a novel multi-conditional generative adversarial network (McGAN) for the road topology refinement
Summary
Road topology reconstruction is a fundamental yet long-standing problem for remote sensing applications [1,2,3], receiving wide attention in the past decades. The width, materials and surrounding environment of rural roads are diverse, which lead to varying spectral performance in the image Such characteristics make it more challenging to reconstruct complete road topological networks for the rural area; for example, disconnection and distortion often appear in extracted road networks [3,11]. A recent road extraction methods, cascaded convolutional neural networks (CasNet) [11], achieves good results by constructing a unified network to extract road region maps and road centerlines. These road extraction methods devoted to constructing end-to-end road extraction frameworks somehow lead to incomplete results, especially facing various road spectral conditions [22]. The experiments demonstrated McGAN can produce a complete road network topology
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