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Improve Road Extraction by Bayesian Data Fusion and Mean Shift Segmentation in Urban Area

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Automatic road extraction is a critical aspect for an effective use of remote-sensing imagery in most contexts. This paper proposes a robust approach based on an existing road extraction method, to provide a better result in urban road extraction. In this contribution, we integrate spectral information from the multispectral image with spatial information from the panchromatic image, to benefit from the spatial properties of high resolution satellite images, using Bayesian data fusion. The pan-sharpened image is then segmented with mean shift technique to weaken the appearance of objects and artifacts on the fused images, while keeping a good image quality to improve road extraction. The quality assessments in the studied urban area show that the completeness and correctness of the extracted major roads in the sense of their lengths were increased by more than 50%, using the Bayesian data fusion method and mean shift filtering. The results of the road extraction are vectorized for GIS integration and for a better interaction with experts.
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Road Extraction; Bayesian Data Fusion; Mean Shift; Image Smoothing; Urban Area; Multi-Source Image Fusion

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