基于高分辨率影像的多尺度最优分割层次模型的城市道路网提取方法
|
摘要
以WorldView 3卫星影像为数据源,综合利用光谱、几何、纹理和上下文等特征,在最优分割的基础上建立区域最优分割参数多层次提取模型,运用数据挖掘等方法提取道路网,基于数学形态学膨胀、腐蚀等方法,对道路提取结果进行断线连接、孔洞填充和细化去毛刺等分类后处理.结果表明,城市道路网提取的完整率、正确率、提取质量分别达到94.42%、89.23%和87.61%.
With WorldView 3 satellite images as the data source, this paper comprehensively utilized spectra, geometric characteristics, texture and context, to set up a regional optimal scale multi-level extraction model on the basis of segmentation image spot and used the method of data mining to extract the road network, based on mathematical morphology, corrosion expansion methods, as well as the road extraction result of bolt connection, holes filling and refined classification reprocessing, deburring, etc.The results showed that the urban road network was extracted with a full rate and good accuracy,with the extracting quality reaching 94.42%, 89.23% and 87.61% respectively.
With WorldView 3 satellite images as the data source, this paper comprehensively utilized spectra, geometric characteristics, texture and context, to set up a regional optimal scale multi-level extraction model on the basis of segmentation image spot and used the method of data mining to extract the road network, based on mathematical morphology, corrosion expansion methods, as well as the road extraction result of bolt connection, holes filling and refined classification reprocessing, deburring, etc.The results showed that the urban road network was extracted with a full rate and good accuracy,with the extracting quality reaching 94.42%, 89.23% and 87.61% respectively.
引文
[1]曹云刚,王志盼,杨磊.高分辨率遥感影像道路提取方法研究进展[J].遥感技术与应用, 2017, 32(1):20-26.
[2]李建.城市道路识别方法研究与实现[D].北京:北方工业大学图书馆, 2017.
[3]裔阳,周绍光,刘文静,等.一种顾及形状特征的遥感图像道路提取方法[J].地理空间信息, 2017, 15(4):47-50.
[4] Blaschke T, Hay G J, Kelly M, et al. Geographic objectbased image analysis:towards a new paradigm[J]. Isprs Journal of Photogrammetry&Remote Sensing, 2014,87(100):180-191.
[5] Blaschke T. Object based image analysis for remote sensing[J]. Isprs Journal of Photogrammetry&Remote Sensing, 2010, 65(1):2-16.
[6]李美玲.基于GF-1遥感影像的道路提取研究[D].北京:北京林业大学图书馆, 2016.
[7]周爱霞,余莉,冯径,等.基于面向对象方法的高分辨率遥感影像道路提取方法研究[J].测绘与空间地理信息,2017(2):1-4.
[8]朱晓铃,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.
[9]姚琴风.多尺度城市道路高分辨率遥感影像提取方法研究[D].太原:太原理工大学图书馆, 2015.
[10] Alshehhi R, Marpu P R. Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images[J]. Isprs Journal of Photogrammetry&Remote Sensing, 2017, 126(2):245-260.
[11] Yin Dan-dong, Du Shi-hong, Wang Shao-wen, et al. A direction-guided ant colony optimization method for extraction of urban road information from very-high-resolution images[J]. IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing, 2016,8(10):4785-4794.
[12] Sameen M I, Pradhan B. A two-stage optimization strategy for fuzzy object-based analysis using airborne lidar and high-resolution orthophotos for urban road extraction[J]. Journal of Sensors, 2017, 2017(6):1-17.
[13]中华人民共和国住房和城乡建设部. CJJ37-2012城市道路工程设计规范[S].北京:中国建筑工业出版社,2016.
[14]林鹏,阮仁宗,王玉强,等.一种基于面向对象的城镇道路自动提取方法研究[J].地理与地理信息科学,2016, 32(1):49-54.
[15]曾发明,杨波,吴德文,等.基于Canny边缘检测算子的矿区道路提取[J].国土资源遥感, 2013, 25(4):72-78.
[16]江帆,刘梦莹,刘勇.综合运用影像对象多种特征的土地利用/土地覆被分类方法探讨:以兰州秦王川地区为例[J].兰州大学学报:自然科学版, 2016, 52(1):37-42.
[17]张毅,谭龙,陈冠,等.基于面向对象分类法的高分辨率遥感滑坡信息提取[J].兰州大学学报:自然科学版,2014, 50(5):745-750.
[18] Singh S M. Simulation of solar zenith angle effect on global vegetation index(GRVI)data[J]. International Journal of Remote Sensing, 1988, 9(2):237-248.
[19] Deering D W, Rouse J W, Haas R H, et al. Measuring'forage production'of grazing units from landsat mss data[J]. Remote Sensing of Environment, 1975(11):1169-1178.
[20] Rouse J W, Haas R H, Schell J A, et al. Monitoring vegetation systems in the great plains with erts[J]. NASA Special Publication, 1974, 351:309-310.
[21] Mcfeeters S K. The use of the normalized difference water index(NDWI)in the delineation of open water features[J]. International Journal of Remote Sensing,2012, 17(7):1425-1432.
[22]赵艺淞,杨昆,王保云,等.随机森林在城市不透水面提取中的应用研究[J].云南师范大学学报:自然科学版, 2017, 37(3):73-78.
[23]孙宇翼,赵军利,王苗苗,等.基于J48决策树的面向对象方法的土地覆被信息提取[J].国土资源遥感, 2016,28(4):156-163.
[24]袁梅宇.数据挖掘与机器学习:WEKA应用技术与实践[M].北京:清华大学出版社, 2014.
[25]刘晓宇. C4.5算法的一种改进及其应用[D].青岛:中国海洋大学图书馆, 2013.
[26]侯俊雄,李琦,朱亚杰,等.基于随机森林的PM2.5实时预报系统[J].测绘科学, 2017, 42(1):1-6.
[27]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):232-233.
[28] Gonzalez R C, Woods R E. Digital image processing[M].2nd Edition. Upper Saddle River:Prentice Hall, 2002.
[29]曹瑾鑫,孙立光.高分辨率遥感图像道路边缘自动检测与提取技术研究[J]. Its通讯, 2006(2):14-18.
[30]邬伦.地理信息系统:原理、方法和应用[M].北京:科学出版社, 2001.
[31]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.
[32] Liu Yong, Bian Ling, Meng Yu-hong, et al. Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis[J]. Isprs Journal of Photogrammetry&Remote Sensing, 2012, 68(1):144-156.
[33]任国贞,江涛.基于灰度共生矩阵的纹理提取方法研究[J].计算机应用与软件, 2014(11):190-192.
[34] Hall M A. Correlation-based feature selection for discrete and numeric class machine learning[C]//Proceedings of the Seventeenth International Conference on Machine Learning, San Francisco:IEEE, 2000:359-366.
[35] Sujatha C, Selvathi D. Connected component-based technique for automatic extraction of road centerline in high resolution satellite images[J]. Eurasip Journal on Image&Video Processing, 2015, 2015(1):8-24.
[2]李建.城市道路识别方法研究与实现[D].北京:北方工业大学图书馆, 2017.
[3]裔阳,周绍光,刘文静,等.一种顾及形状特征的遥感图像道路提取方法[J].地理空间信息, 2017, 15(4):47-50.
[4] Blaschke T, Hay G J, Kelly M, et al. Geographic objectbased image analysis:towards a new paradigm[J]. Isprs Journal of Photogrammetry&Remote Sensing, 2014,87(100):180-191.
[5] Blaschke T. Object based image analysis for remote sensing[J]. Isprs Journal of Photogrammetry&Remote Sensing, 2010, 65(1):2-16.
[6]李美玲.基于GF-1遥感影像的道路提取研究[D].北京:北京林业大学图书馆, 2016.
[7]周爱霞,余莉,冯径,等.基于面向对象方法的高分辨率遥感影像道路提取方法研究[J].测绘与空间地理信息,2017(2):1-4.
[8]朱晓铃,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.
[9]姚琴风.多尺度城市道路高分辨率遥感影像提取方法研究[D].太原:太原理工大学图书馆, 2015.
[10] Alshehhi R, Marpu P R. Hierarchical graph-based segmentation for extracting road networks from high-resolution satellite images[J]. Isprs Journal of Photogrammetry&Remote Sensing, 2017, 126(2):245-260.
[11] Yin Dan-dong, Du Shi-hong, Wang Shao-wen, et al. A direction-guided ant colony optimization method for extraction of urban road information from very-high-resolution images[J]. IEEE Journal of Selected Topics in Applied Earth Observations&Remote Sensing, 2016,8(10):4785-4794.
[12] Sameen M I, Pradhan B. A two-stage optimization strategy for fuzzy object-based analysis using airborne lidar and high-resolution orthophotos for urban road extraction[J]. Journal of Sensors, 2017, 2017(6):1-17.
[13]中华人民共和国住房和城乡建设部. CJJ37-2012城市道路工程设计规范[S].北京:中国建筑工业出版社,2016.
[14]林鹏,阮仁宗,王玉强,等.一种基于面向对象的城镇道路自动提取方法研究[J].地理与地理信息科学,2016, 32(1):49-54.
[15]曾发明,杨波,吴德文,等.基于Canny边缘检测算子的矿区道路提取[J].国土资源遥感, 2013, 25(4):72-78.
[16]江帆,刘梦莹,刘勇.综合运用影像对象多种特征的土地利用/土地覆被分类方法探讨:以兰州秦王川地区为例[J].兰州大学学报:自然科学版, 2016, 52(1):37-42.
[17]张毅,谭龙,陈冠,等.基于面向对象分类法的高分辨率遥感滑坡信息提取[J].兰州大学学报:自然科学版,2014, 50(5):745-750.
[18] Singh S M. Simulation of solar zenith angle effect on global vegetation index(GRVI)data[J]. International Journal of Remote Sensing, 1988, 9(2):237-248.
[19] Deering D W, Rouse J W, Haas R H, et al. Measuring'forage production'of grazing units from landsat mss data[J]. Remote Sensing of Environment, 1975(11):1169-1178.
[20] Rouse J W, Haas R H, Schell J A, et al. Monitoring vegetation systems in the great plains with erts[J]. NASA Special Publication, 1974, 351:309-310.
[21] Mcfeeters S K. The use of the normalized difference water index(NDWI)in the delineation of open water features[J]. International Journal of Remote Sensing,2012, 17(7):1425-1432.
[22]赵艺淞,杨昆,王保云,等.随机森林在城市不透水面提取中的应用研究[J].云南师范大学学报:自然科学版, 2017, 37(3):73-78.
[23]孙宇翼,赵军利,王苗苗,等.基于J48决策树的面向对象方法的土地覆被信息提取[J].国土资源遥感, 2016,28(4):156-163.
[24]袁梅宇.数据挖掘与机器学习:WEKA应用技术与实践[M].北京:清华大学出版社, 2014.
[25]刘晓宇. C4.5算法的一种改进及其应用[D].青岛:中国海洋大学图书馆, 2013.
[26]侯俊雄,李琦,朱亚杰,等.基于随机森林的PM2.5实时预报系统[J].测绘科学, 2017, 42(1):1-6.
[27]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):232-233.
[28] Gonzalez R C, Woods R E. Digital image processing[M].2nd Edition. Upper Saddle River:Prentice Hall, 2002.
[29]曹瑾鑫,孙立光.高分辨率遥感图像道路边缘自动检测与提取技术研究[J]. Its通讯, 2006(2):14-18.
[30]邬伦.地理信息系统:原理、方法和应用[M].北京:科学出版社, 2001.
[31]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.
[32] Liu Yong, Bian Ling, Meng Yu-hong, et al. Discrepancy measures for selecting optimal combination of parameter values in object-based image analysis[J]. Isprs Journal of Photogrammetry&Remote Sensing, 2012, 68(1):144-156.
[33]任国贞,江涛.基于灰度共生矩阵的纹理提取方法研究[J].计算机应用与软件, 2014(11):190-192.
[34] Hall M A. Correlation-based feature selection for discrete and numeric class machine learning[C]//Proceedings of the Seventeenth International Conference on Machine Learning, San Francisco:IEEE, 2000:359-366.
[35] Sujatha C, Selvathi D. Connected component-based technique for automatic extraction of road centerline in high resolution satellite images[J]. Eurasip Journal on Image&Video Processing, 2015, 2015(1):8-24.