An improved road extraction method for remote sensing images based on Canny edge detection

doi:10.6046/gtzyyg.2019.01.09

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Abstract

The extraction method based on the edge features is widely used in the road recognition of remote sensing image. However, the traditional methods are not good at eliminating noise, and tend to cause the misjudgment and leak-judgment of the edge. Therefore, based on the idea of the canny edge detection algorithm, the authors firstly adopt a smoothing and self-adapting Gaussian filter to reduce the noise of remote sensing image, reduce the noise interference and reserve the edge and details. Then, in the edge judgment of the dual threshold, the authors select the high and low thresholds on the basis of local characteristics within the object scale of the pixel point and enhance the exact judgment performance of the edge. The experiment results show that the new method can effectively improve the accuracy and positioning accuracy of the edge detection, obviously reduce the misjudgment of road edge extraction and remarkably increase integrity and consecutiveness, with high automation.

Keywords remote sensing images road edge Canny algorithm Gauss filter adaptive dual threshold

TP391

Corresponding Authors: Huixian HUANG E-mail: huanghx@xtu.edu.cn

Issue Date: 15 March 2019

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	Wei HUANG
	Huixian HUANG
	Jianmin XU
	Jiating LIU

Cite this article:

Wei HUANG,Huixian HUANG,Jianmin XU, et al. An improved road extraction method for remote sensing images based on Canny edge detection[J]. Remote Sensing for Land & Resources, 2019, 31(1): 65-70.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2019.01.09 OR https://www.gtzyyg.com/EN/Y2019/V31/I1/65

Fig.1 Neighbourhood of R=2

Fig.2 Contrast without noise of image

Fig.3 Contrast of image with Gauss noise and salt and pepper noise

Fig.4 Figure of merit versus Gauss noise density

Fig.5 Figure of merit versus salt and pepper noise density

Fig.6 Results of image detection

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