Extraction of road edge lines from remote sensing image based on image blocking and line segment voting

doi:10.6046/gtzyyg.2015.01.09

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Abstract

Edge lines extraction from remote sensing images is a classic problem, and different edge extraction algorithms are applicable to different types of images. The road shape is not very regular, the contrast is low and the impact of noise is serious in actual remote sensing image because the road might be blocked by buildings and trees, and the road edge lines are likely to be broken; therefore road edge lines extraction from high-resolution remote sensing image is always a hot research topic. In this paper,the authors propose a new method for extraction of the road lines from remote sensing image so as to solve the problem that it is difficult for the methods available to extract clear and continuous road edge lines. Firstly, the direction templates are introduced to detect the edge points and search for the sub-segments in block image; then the sub-segments are extended and the line segment voting is taken to connect straight line segments in the curved edge lines, and the edge lines whose length is greater than a given threshold are output; finally, the spur and bifurcation are removed and the union of edge lines in eight directions is taken as the final road network. Experiment results show that the method proposed in this paper can be used to extract the road edge lines which have a certain curvature and low contrast and are affected by noise seriously from high-resolution remote sensing images.

Keywords FY-3 satellite soil moisture retrieval by remote sensing

:	TP751.1
	P237

Issue Date: 08 December 2014

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	BAO Yansong
	MAO Fei
	MIN Jinzhong
	WANG Dongmei
	YAN Jing

Cite this article:

BAO Yansong,MAO Fei,MIN Jinzhong, et al. Extraction of road edge lines from remote sensing image based on image blocking and line segment voting[J]. REMOTE SENSING FOR LAND & RESOURCES, 2015, 27(1): 55-61.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2015.01.09 OR https://www.gtzyyg.com/EN/Y2015/V27/I1/55

[1] 明冬萍,骆剑承,沈占锋,等.高分辨率遥感影像信息提取与目标识别技术研究[J].测绘科学,2005,30(3):18-20. Ming D P,Luo J C,Shen Z F,et al.Research on information extraction and target recognition from high resolution remote sensing image[J].Science of Surveying and Mapping,2005,30(3):18-20.

[2] 刘晓沐,季艳,万寿红.单源可见光遥感图像中的水域识别[J].仪器仪表学报,2006,27(6):2086-2087. Liu X M,Ji Y,Wan S H.Waters recognition in single-source visible remote sensing image[J].Chinese Journal of Scientific Instrument,2006,27(6):2086-2087.

[3] 张伟伟,毛政元.一种面向高空间分辨率遥感影像的路口自动定位新方法[J].国土资源遥感,2012,24(1):13-16. Zhang W W,Mao Z Y.A new approach to automatic positioning of road junctions in high spatial resolution remotely sensed imagery[J]. Remote Sensing for Land and Resources,2012,24(1):13-16.

[4] 高芳琴,吴健平.遥感图像识别中线性地物提取方法研究[J].仪器仪表学报,2005,25(4):653-656. Gao F Q,Wu J P.Detecting linear object in remote sensing image recognition[J].Chinese Journal of Scientific Instrument,2005,25(4):653-656.

[5] 吴亮,胡云安.遥感图像自动道路提取方法综述[J].自动化学报,2010,36(7):912-922. Wu L,Hu Y A.A survey of automatic road extraction from remote sensing images[J].Aota Automatica Sinica,2010,36(7):912-922.

[6] 董占杰,毛政元.基于道路绿地特征的遥感影像道路信息提取方法研究[J].国土资源遥感,2011,23(2):43-46. Dong Z J,Mao Z Y.Methodological research on road extraction based on characteristics of road greenbelts in remotely sensed imagery[J].Remote Sensing for Land and Resources,2011,23(2):43-46.

[7] 钟家强,王润生.一种基于线特征的道路网变化检测算法[J].遥感学报,2007,11(1):27-32. Zhong J Q,Wang R S.A road network change detection algorithm based on linear feature[J].Journal of Remote Sensing,2007,11(1):27-32.

[8] 肖鹏峰,冯学智,赵书河,等.一种基于相位一致的高分辨率遥感图像特征检测方法[J].遥感学报,2007,11(3):303-310. Xiao P F,Feng X Z,Zhao S H,et al.Feature detection from high-resolution remotely sensed imagery based on phase[J].Journal of Remote Sensing,2007,11(3):303-310.

[9] 朱昌盛,周伟,关键.基于平行线对检测的SAR图像主干道提取算法[J].中国图形图象学报,2011,16(10):1908-1917. Zhu C S,Zhou W,Guan J.Main roads extraction from SAR imagery based on parallel pairs detection[J].Journal of Image and Graphics,2011,16(10):1908-1917.

[10] 梅小明,张良培,李平湘.利用Beamlet变换算法提取遥感图像直线[J].计算机科学,2007,34(12):230-232. Mei X M,Zhang L P,Li P X.Line extraction in remote sensing image with Beamlet transform[J].Computer Science,2007,34(12):230-232.

[11] 贾承丽,匡纲要,粟毅.基于Hough变换的高分辨SAR图像道路目标检测[J].国防科技大学学报,2009,26(1):50-55. Jia C L,Kuang G Y,Su Y.Road extraction from the high-resolution SAR imagery using Hough transform[J].Journal of National University of Defense Technology,2009,26(1):50-55.

[12] 雷小奇,王卫星,赖均.一种基于形状特征进行高分辨率遥感影像道路提取方法[J].测绘学报,2009,38(5):457-465. Lei X Q,Wang W X,Lai J.A method of road extraction from high-resolution remote sensing images based on shape features[J].Acta Geodaetica et Cartographica Sinica,2009,38(5):457-465.

[13] 李燕,余旭初.基于启发式图搜索的遥感影像道路半自动提取[J].测绘学院学报,2002,19(4):268-271. Li Y,Yu X C.Semi-automatic extraction of roads from satellite imagery based on heuristic graph search[J].Journal of Institute of Surveying and Mapping,2002,19(4):268-271.

[14] 周绍光,徐勇.在高分辨率遥感影像中提取无清晰连续边缘线的道路[J].测绘学报,2008,37(3):301-307. Zhou S G,Xu Y.To extract roads with no clear and continuous boundaries in remote sensing images[J].Acta Geodaetica et Cartographica Sinica,2008,37(3):301-307.

[15] 韩秋蕾,朱明,姚志军.基于改进Hough变换的图像线段特征提取[J].仪器仪表学报,25(4):436-439. Han Q L,Zhu M,Yao X J.Line segment feature extraction based on improved Hough transform[J].Chinese Journal of Scientific Instrument,25(4):436-439.

[16] Martin D R,Fowlkes C C,Malik J.Learning to detect natural image boundaries using local brightness,color,and texture cues[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2004,26(5):530-549.

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