Extracting road networks from high-resolution remote sensing images using multi features and methods

doi:10.6046/gtzyyg.2018.03.05

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Abstract

Roads on the high-resolution remote sensing images perform the stripe homogeneous region with ribbon-like shape and approximate width. According to these features, this paper presents a simple yet effective method of delineating road networks from high-resolution remote sensing images, which combines multi features and methods. 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, much better than the method proposed by Miao et al.

Keywords high-resolution remote sensing image homogeneity mean shift road networks construction

TP79

Corresponding Authors: Fanzhi CAO E-mail: 513572289@qq.com

Issue Date: 10 September 2018

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	Runsheng LI
	Fanzhi CAO
	Wen CAO
	Shuxiang WANG

Cite this article:

Runsheng LI,Fanzhi CAO,Wen CAO, et al. Extracting road networks from high-resolution remote sensing images using multi features and methods[J]. Remote Sensing for Land & Resources, 2018, 30(3): 33-39.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.03.05 OR https://www.gtzyyg.com/EN/Y2018/V30/I3/33

Fig.1 Flow chart of road networks extraction

Fig.2 Steady-state point map of strip-shaped region

Fig.3 Process of stick voting

Fig.4 The first experiment of road networks extraction

Fig.5 The second experiment of road networks extraction

Fig.6 Comparative experiment of road extraction

Tab.1 Evaluation results of road extraction accuracy(%)

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