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Proceedings Paper

A research of road centerline extraction algorithm from high resolution remote sensing images
Author(s): Yushan Zhang; Tingfa Xu
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Paper Abstract

Satellite remote sensing technology has become one of the most effective methods for land surface monitoring in recent years, due to its advantages such as short period, large scale and rich information. Meanwhile, road extraction is an important field in the applications of high resolution remote sensing images. An intelligent and automatic road extraction algorithm with high precision has great significance for transportation, road network updating and urban planning. The fuzzy c-means (FCM) clustering segmentation algorithms have been used in road extraction, but the traditional algorithms did not consider spatial information. An improved fuzzy C-means clustering algorithm combined with spatial information (SFCM) is proposed in this paper, which is proved to be effective for noisy image segmentation. Firstly, the image is segmented using the SFCM. Secondly, the segmentation result is processed by mathematical morphology to remover the joint region. Thirdly, the road centerlines are extracted by morphology thinning and burr trimming. The average integrity of the centerline extraction algorithm is 97.98%, the average accuracy is 95.36% and the average quality is 93.59%. Experimental results show that the proposed method in this paper is effective for road centerline extraction.

Paper Details

Date Published: 19 September 2017
PDF: 11 pages
Proc. SPIE 10396, Applications of Digital Image Processing XL, 103961W (19 September 2017); doi: 10.1117/12.2271748
Show Author Affiliations
Yushan Zhang, Beijing Institute of Technology (China)
Tingfa Xu, Beijing Institute of Technology (China)


Published in SPIE Proceedings Vol. 10396:
Applications of Digital Image Processing XL
Andrew G. Tescher, Editor(s)

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