Optimized method for road extraction from high resolution remote sensing image based on watershed algorithm

doi:10.6046/gtzyyg.2013.03.05

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Abstract

To tackle the problems existent in road information extraction from high resolution remote sensing, the authors put forward an improved approach to road extraction based on watershed segmentation according to the basic theories of object-oriented method and mathematical morphology. Firstly, the image is processed by improved watershed segmentation to extract basic road information after preprocessing. Then object-oriented method is used to extract road per-parcel so as to optimize the road extraction results. Finally, after binary image processing, the incomplete results can be removed and corrected by using mathematical morphological transformation. Experimentation shows that the proposed method can extract urban road information efficiently and process the roads from the complex urban context fairly satisfactorily.

Keywords UAV image land use information extraction object-oriented

TP 75

Issue Date: 03 July 2013

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	HE Shaolin
	XU Jinghua
	ZHANG Shuaiyi

Cite this article:

HE Shaolin,XU Jinghua,ZHANG Shuaiyi. Optimized method for road extraction from high resolution remote sensing image based on watershed algorithm[J]. REMOTE SENSING FOR LAND & RESOURCES, 2013, 25(3): 25-29.

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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2013.03.05 OR https://www.gtzyyg.com/EN/Y2013/V25/I3/25

[1] 胡翔云,张祖勋,张剑清.航空影象上线状地物的半自动提取[J].中国图像图形学报,2002,7(2):137-140. Hu X Y,Zhang Z X,Zhang J Q.Semiautomatic extraction of linear object from aerial image[J].Journal of Image and Graphics,2002,7(2):137-140.

[2] 丁美琳,李光耀,张巧芳.Snakes模型在卫星图片道路提取中的应用[J].计算机技术与发展,2010,20(1):71-73. Ding M L,Li G Y,Zhang Q F.Application of road extraction in satellite images based on snakes model[J].Computer Technology and Development,2010,20(1):71-73.

[3] 张睿,张继贤,李海涛.基于角度纹理特征及剖面匹配的高分辨率遥感影像带状道路半自动提取[J].遥感学报,2008,12(2):224-232. Zhang R,Zhang J X,Li H T.Semi-automatic extraction of ribbon roads from high resolution remotely sensed imagery based on angular texture signature and profile match[J].Journal of Remote Sensing,2008,12(2):224-232.

[4] 林丽群,肖俊.结合先验知识和图像特征的道路提取方法[J].计算机工程与应用,2011,47(33):236-239. Lin L Q,Xiao J.Combined prior knowledge and image characteristics for lane detection method[J].Computer Engineering and Applications,2011,47(33):236-239.

[5] 宁亚辉,雷小奇,王功孝,等.Dempster-Shafer证据融合形状特征的高分辨率遥感图像道路信息提取[J].中国图象图形学报,2011,16(12):2183-2190. Ning Y H,Lei X Q,Wang G X ,et al.Road extraction from high-resolution remote sensing images based on Dempster-Shafer evidence theory and fusion shape features[J].Journal of Image and Graphics,2011,16(12):2183-2190.

[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] 马欢,楚恒,王汝言.纹理与几何特征在道路提取中的应用[J].计算机与现代化,2012(7):96-99. Ma H,Chu H,Wang R Y.Application of texture and geometrical characteristics in road extraction[J].Computer and Modernization,2012(7):96-99.

[8] 卢昭羿,左小清,黄亮,等.面向对象的投影互分割道路变化检测[J].国土资源遥感,2012,24(3):60-64. Lu Z Y,Zuo X Q,Huang L,et al.Road change detection using object-oriented projective interactive partition[J].Remote Sensing for Land and Resources,2012,24(3):60-64.

[9] 许高程,毕建涛,王星星,等.面向对象的高分辨率遥感影像道路自动提取实验[J].遥感信息,2012(2):108-111. Xu G C,Bi J T,Wang X X,et al.An experiment of automatic road extraction from high-resolution remote sensing image based on object-oriented technology[J].Remote Sensing Information,2012(2):108-111.

[10] 刘小丹,刘岩.基于Hough变换和路径形态学的城区道路提取[J].计算机工程,2012,38(6):265-268. Liu X D,Liu Y.Urban road extraction based on Hough transform and path morphology[J].Computer Engineering,2012,38(6):265-268.

[11] 杨先武,韦春桃,吕建刚,等.基于形态重建的高分辨率遥感影像城市道路提取[J].测绘通报,2012(1):45-47. Yang X W,Wei C T,Lü J G,et al.Urban road extraction of high resolution remote sensing images based on shape reconstruction[J].Bulletin of Surveying and Mapping,2012(1):45-47.

[12] 潘婷婷,李朝峰.基于区域生长型分水岭算法的卫星图像道路提取方法[J].计算机工程与设,2008,29(19):4987-5013. Pan T T,Li C Z.Road extraction method of satellite image based on region-growth watersheds algorithm[J].Computer Engineering and Design,2008,29(19):4987-5013.

[13] 罗庆洲,尹球,匡定波.光谱与形状特征相结合的道路提取方法研究[J].遥感技术与应用,2007,22(3):339-344. Luo Q Z,Yin Q,Kuang D B.Research on extracting road based on its spectral feature and shape feature[J].Remote Sensing Technology and Application,2007,22(3):339-344.

[14] 朱晓玲,邬群勇.基于高分辨率遥感影像的城市道路提取方法研究[J].资源环境与工程,2009,23(3):296-299. Zhu X L,Wu Q Y.Study on the method of extracting city roads from the high resolution remote sensing image[J].Resources Environment and Engineering,2009,23(3):296-299.

[15] 潘建平,邬明权.基于数学形态学的道路提取[J].计算机工程与应用,2008,44(11):232-240. Pan J P,Wu M Q.Road detection based on morphology[J].Computer Engineering and Application,2008,44(11):232-240.

[16] 胡海旭,王文,何厚军.基于纹理特征与数学形态学的高分辨率影像城市道路提取[J].地理与地理信息科学,2008,24(6):46-49. Hu H X,Wang W,He H J.City road extraction from high resolution remote sensing imagery based on texture and mathematical morphology[J].Geography and Geo-Information Science,2008,24(6):46-49.

[17] Vincent L.Morphological grayscale reconstruction in image analysis:Applications and efficient algorithms[J].IEEE Transaction on Image Process,1993,2(2):176-201.

[18] 申邵洪,宋杨,万幼川,等.高分辨率多光谱遥感影像中城区道路信息的自动提取[J].遥感应用,2007(8):73-77. Shen S H,Song Y,Wan Y C,et al.An approach for automatic road extraction in dense urban area from high-resolution multispectral satellite imagery[J].Remote Sensing Information,2007(8):73-77.

[19] Gaetano R,Zerubia J,Scarpa G.Morphological road segmentation in urban areas from high resolution satellite images［C］//Digital Signal Processing(DSP).Corfu Greece:IEEE,2011:1-8.

[20] Zhu C,Shi W,Pesaresi M.The recognition of road network from high-resolution satellite remotely sensed data using image morphological characteristics[J].International Journal of Remote Sensing,2005,26(24):5493-5508.

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