Abstract
In recent decades, road extraction from very high-resolution (VHR) remote sensing images has become popular and has attracted extensive research efforts. However, the very high spatial resolution, complex urban structure, and contextual background effect of road images complicate the process of road extraction. For example, shadows, vehicles, or other objects may occlude a road located in a developed urban area. To address the problem of occlusion, this study proposes a semiautomatic approach for road extraction from VHR remote sensing images. First, guided image filtering is employed to reduce the negative effects of nonroad pixels while preserving edge smoothness. Then, an edge-constraint-based weighted fusion model is adopted to trace and refine the road centerline. An edge-constraint fast marching method, which sequentially links discrete seed points, is presented to maintain road-point connectivity. Six experiments with eight VHR remote sensing images (spatial resolution of 0.3 m/pixel to 2 m/pixel) are conducted to evaluate the efficiency and robustness of the proposed approach. Compared with state-of-the-art methods, the proposed approach presents superior extraction quality, time consumption, and seed-point requirements.
Highlights
Accurate and up-to-date road network information is extremely critical for various urban applications, such as navigation and infrastructure maintenance [1,2,3]
The present study indicates that the synergy of edge information, road centerline probability map, and road spectral feature can overcome the shortcomings of the bias of the road centerline extracted by the fast marching method, which uses spectral feature only
The results obtained through the method without edge constraint deviated from the true road centerline, whereas those obtained through the proposed method with edge constraint could preserve the road centerline
Summary
Accurate and up-to-date road network information is extremely critical for various urban applications, such as navigation and infrastructure maintenance [1,2,3]. The advent of modern remote sensing has enabled the extraction of information from very high-resolution (VHR) and highly detailed optical images of roads to update urban road networks [4,5]. State-of-the-art methods for road-feature extraction from VHR images fall into two categories: Automatic and semiautomatic methods. Automatic approaches require no prior information and can be executed by a series of image-processing algorithms, such as mathematical morphology [11,12], active snake model [13], dynamic programming [14], neural networks [15,16,17], probabilistic graphical models [18], filtering-based methods [19], and object-oriented methods [20]. In contrast to automatic methods, semiautomatic methods require user input or other prior information to achieve robust and stable results
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