Abstract
Automatic road network detection from multispectral imagery is an effective and economic way to obtain the road and related information. This paper presents an integrated method to extract road network centerline from multispectral imagery. It includes four main steps. First, support vector machine (SVM) is used to analyze spectral information to classify the imagery into road and non-road regions. Then, shape feature, morphological top-hat transform and multiple directional filters are cascaded to reduce the misclassification. Based on these procedures, morphological thinning algorithm and Hough transform are introduced to detect the road centerline which will be regarded as road primitives. Finally, a novel road tracking method is developed to refine and improve the whole road network. The proposed method is verified on a multispectral images acquired from SPOT-6 satellite and QuickBird data sets.
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Du, Y., Li, J., Wang, Y. (2016). An Integrated Method for Road Network Centerline Detection from Multispectral Imagery. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2016. IDEAL 2016. Lecture Notes in Computer Science(), vol 9937. Springer, Cham. https://doi.org/10.1007/978-3-319-46257-8_36
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DOI: https://doi.org/10.1007/978-3-319-46257-8_36
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