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Road Network Extraction from High-Resolution Remote Sensing Image Using Homogenous Property and Shape Feature

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Abstract

According to spectral homogeneity and ribbon-like shape of road, this letter presents a simple yet effective method of delineating road networks from high-resolution remote sensing images. The proposed method consists of three main steps. First, the mean shift algorithm is utilized to detect the modes of density of image points in spectral–spatial space which contain potential road center points and then detected mode points are classified into different classes by mean shift-based clustering on the basis of spectral information. Next, the combination of Gabor filtering and tensor encoding is used to identify the road class and to extract road center points. Lastly, road network is generated from detected road center points by means of tensor voting and connected component analysis. The experimental results demonstrate good performances of the proposed method in road network extraction from high-resolution remote sensing images.

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Authors and Affiliations

  1. State Key Laboratory of Geo-information Engineering, Xi’an, 710000, China

    Runsheng Li

  2. Information Engineering University, Zhengzhou, 450052, China

    Runsheng Li & Fanzhi Cao

Authors
  1. Runsheng Li
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  2. Fanzhi Cao
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Correspondence to Runsheng Li.

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Li, R., Cao, F. Road Network Extraction from High-Resolution Remote Sensing Image Using Homogenous Property and Shape Feature. J Indian Soc Remote Sens 46, 51–58 (2018). https://doi.org/10.1007/s12524-017-0678-6

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  • DOI: https://doi.org/10.1007/s12524-017-0678-6

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