城市建成区遥感影像边界提取与扩张分析
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摘要
针对城市建成区提取过程中,仅依赖单一数据源导致精度不够的问题,本文基于面向对象分类方法和利用土地类型信息标准差统计变量,实现遥感影像中城市建城区边界的提取,并以该建成区为依据对河南省虞城县的城区空间扩张特征作了分析。实验中首先采用均值漂移分割算法对高分一号遥感影像实现分割,然后利用决策树分类算法实现土地利用类型分类,最后基于0.1 km×0.1 km窗口统计土地利用类型标准差信息,获取建成区边界。面向实际应用,以河南省虞城县为例,采用高分一号影像获得虞城县2017年建成区数据,并基于该数据采用多个TM影像提取城区其他年份的建成区边界,实现河南省虞城县城区空间扩张特征分析。结果表明,本文方法获取的建成区边界精度较一般的监督分类提取边界有进一步的提高,精度达到89%。进而说明结合高分辨率影像提取多个年份的建成区数据的可靠性,在城市扩张研究中,对仅利用低空间分辨率提取精度不够问题和仅利用高分辨影像提取效率低等问题提供了较好的解决方案。
Urban expansion is a complex time-conversion process, in which the different land use types are converted into urban land. The expansion of urban land use affects not only the development of the city itself,but also has a great impact on the ecological environment in urban areas. Taking into account the insufficient accuracy of urban built-up area extraction from a single data source alone, this paper analyzed the urban spatial expansion characteristics of Yucheng County in Henan Province based on the built-up area extracted by the decision tree classification and statistics variable extraction method from remote sensing imagery. In this experiment, the mean shift segmentation algorithm is used to segment the high spatial resolution image(GF1)firstly, then, based on the decision tree classification algorithm, image segmentation blocks were classified into three categories: cultivated land, water and construction land. Finally, the standard deviation information based on the land use type in 0.1 km × 0.1 km window is used to obtain built-up boundary. Taking Yucheng County as an example, the built-up area data is firstly extracted from high-resolution GF1 image(2017) and then, based on this data, we got the built-up area boundary of Yucheng county in 2000, 2009 and 2013 from temporal TM images, and finally analyzed urban expansion characteristic. The results show that the data of built-up area extracted from high spatial resolution imagery is more reliable by mentioned method. In this paper, the boundary of the built-up area has been further improved by the generalized supervised classification extraction method,with an accuracy of 89%. This paper showed a trade-off in the study of urban expansion to solve the problem of the low accuracy caused by only depended on low spatial resolution image and the low efficiency caused by only depended on high spatial resolution imagery to extract built-up area by using one high-resolution image and multiple low-resolution images.
Urban expansion is a complex time-conversion process, in which the different land use types are converted into urban land. The expansion of urban land use affects not only the development of the city itself,but also has a great impact on the ecological environment in urban areas. Taking into account the insufficient accuracy of urban built-up area extraction from a single data source alone, this paper analyzed the urban spatial expansion characteristics of Yucheng County in Henan Province based on the built-up area extracted by the decision tree classification and statistics variable extraction method from remote sensing imagery. In this experiment, the mean shift segmentation algorithm is used to segment the high spatial resolution image(GF1)firstly, then, based on the decision tree classification algorithm, image segmentation blocks were classified into three categories: cultivated land, water and construction land. Finally, the standard deviation information based on the land use type in 0.1 km × 0.1 km window is used to obtain built-up boundary. Taking Yucheng County as an example, the built-up area data is firstly extracted from high-resolution GF1 image(2017) and then, based on this data, we got the built-up area boundary of Yucheng county in 2000, 2009 and 2013 from temporal TM images, and finally analyzed urban expansion characteristic. The results show that the data of built-up area extracted from high spatial resolution imagery is more reliable by mentioned method. In this paper, the boundary of the built-up area has been further improved by the generalized supervised classification extraction method,with an accuracy of 89%. This paper showed a trade-off in the study of urban expansion to solve the problem of the low accuracy caused by only depended on low spatial resolution image and the low efficiency caused by only depended on high spatial resolution imagery to extract built-up area by using one high-resolution image and multiple low-resolution images.
引文
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[12]周芳芳,樊晓平,叶榛.均值漂移算法的研究与应用[J].控制与决策,2007,22(8):841-847.[Zhou F F,Fan X P,Ye Z.Mean shift research and applications[J].Control and Decision,2007,22(8):841-847.]
[13]林辉,莫登奎,熊育久,等.高分辨率遥感图像均值调整法分割技术研究[J].中南林业科技大学学报,2006,26(4):85-88.[Lin H,Mo D K,Xiong Y J,et al.The Method of Segmenting High-resolution Remote Sensing Images Based on Mean-shift[J].Journal of Central South Forestry University,2006,26(4):85-88.]
[14]Bankers-Fulbright J L,Kita H,et al.A remote sensing image segmentation method based on spectral and texture information fusion[C].International Conference on Information Technology:New Generations,2009.
[15]Wu X D,Kumar V,Quinlan J R,et al.Top 10 algorithms in data mining[J].Knowledge and Information Systems,2008,14(1):1-37.
[16]张剑清,段艳.一种结合分水岭与决策树C5.0的极化SAR分类方法[J].武汉大学学报·信息科学版,2014,39(8):891-896.[Zhang J Q,Duan Y.A supervised classification method of polarimetric sythetic aperture radar data using watershed segmentation and decision tree C5.0[J].Journal of Geo-Information Science,2014,39(8):891-896.]
[17]李海江,刘耀林,谭荣辉,等.一种新的基于高分辨率全色影像的城市建成区边界提取算法[J].测绘通报,2015(7):36-41.[Li H J,Liu Y L,Tan R H,et al.A new algorithm of urban built-up area boundary extraction based on highresolution panchromatic image[J].Bulletin of Surveying and Mapping,2015(7):36-41.]
[18]范作江.遥感与地理信息系统相结合的城市扩展研究——以北京市十年城市扩展为例[D].北京:北京大学,1996.[Fan Z J.Study of urban expansion based on remote sensing and geography information system[D].Beijing:Beijing University,1996.]
[19]魏斌.基于GIS和RS的城市空间扩展研究分析[D].烟台:鲁东大学,2014.[Wei B.Extended research and analysis of the GIS and RS based on city space:A case of study of Qingdao[D].Yantai:Ludong University,2014.]
[20]刘纯,洪亮,周光海.基于TM影像的衡阳市城区扩展趋势研究[J].昆明冶金高等专科学校学报,2013,29(5):15-19.[Liu C,Hong L,Zhou G H.A study on urban sprawl in Hengyang city based on TM image[J].Journal of Kunming Metallurgy College,2013,29(5):15-19.]
[2]陈淑兴,韩波,柯长青,等.济南市建成区扩展的时空特征及驱动力分析[J].地理空间信息,2009,7(5):117-120.[Chen S X,Han B,Ke C Q,et al.Spatial-temperal characteristics and driving force analysis in built-up areas expansion of Jinan[J].Geospatial Information,2009,7(5):117-120.]
[3]张雪峰,杨晏立,何政伟,等.苏、锡、常建成区遥感方法提取及城市扩展分析[J].测绘科学,2011,36(2):113-115.[Zhang X F,Yang Y L,He Z W,et al.Built-up area extraction by RS and urban expansion analysis of Suzhou,Wuxi,and Changzhou[J].Science of Surveying and Mapping,2011,36(2):113-115.]
[4]杨小芳,代侦勇,李瑞敏,等.基于GIS城市建成区范围界定方法实证研究——以奉化建成区为例[J].测绘与空间地理信息,2017(11):103-104.[Yang X F,Dai Z Y,Li R M,et al.Empirical study on the method of defining urban built-up area based on GIS:Take Fenghua built-up area as an example[J].Geomatics and Spatial Information Technology,2017(11):103-104.]
[5]李天华,马玲,杨武年,等.应用遥感和GIS技术提取城市建成区及驱动力分析——以南京市为例[J].测绘科学,2007,32(4):124-125.[Li T H,Ma L,Yang W N,et al.The analysis on urban expansion based on Remote Sensing and GIS:A case of Nanjing city[J].Science of Surveying and Mapping,2007,32(4):124-125.]
[6]Liu J,Li P,Wang X.A new segmentation method for very high-resolution imagery using spectral and morphological information[J].ISPRS Journal of Photogrammetry and Remote Sensing,2015,101(6):145-162.
[7]Wang C,Shi A Y,Wang X,et al.A novel multi-scale segmentation algorithm for high resolution remote sensing images based on wavelet transform and improved JSEG algorithm[J].Optik-International Journal for Light and Electron Optics,2014,125(19):5588-5595.
[8]Zhang X,Xiao P,Feng X,et al.Hybrid region merging method for segmentation of high-resolution remote sensing images[J].ISPRS Journal of Photogrammetry and Remote Sensing,2014,98:19-28.
[9]邓富亮,杨崇俊,曹春香,等.高分辨率影像分割的分形网络演化改进方法[J].地球信息科学学报,2014,16(1):95-101.[Deng F L,Yang C J,Cao C X,et al.An improved method of FNEA for high resolution remote sensing image segmentation[J].Journal of Geo-Information Science,2014,16(1):95-101.]
[10]沈占锋,骆剑承,胡晓东,等.高分辨率遥感影像多尺度均值漂移分割算法研究[J].武汉大学学报·信息科学版,2010,35(3):313-317.[Shen Z F,Luo J C,Hu X D,et al.A mean shift multi-scale segmentation for high-resolution remote sensing images[J].Geomatics and Information Science of Wuhan University,2010,35(3):313-317.]
[11]李艳灵,沈轶.基于KNN的均值漂移图像分割算法[J].华中科技大学学报·自然科学版,2009,37(10):68-71.[Li Y L,Shen Y.KNN based mean shift algorithm for image segmentation[J].Huazhong University of Science and Technology,2009,37(10):68-71.]
[12]周芳芳,樊晓平,叶榛.均值漂移算法的研究与应用[J].控制与决策,2007,22(8):841-847.[Zhou F F,Fan X P,Ye Z.Mean shift research and applications[J].Control and Decision,2007,22(8):841-847.]
[13]林辉,莫登奎,熊育久,等.高分辨率遥感图像均值调整法分割技术研究[J].中南林业科技大学学报,2006,26(4):85-88.[Lin H,Mo D K,Xiong Y J,et al.The Method of Segmenting High-resolution Remote Sensing Images Based on Mean-shift[J].Journal of Central South Forestry University,2006,26(4):85-88.]
[14]Bankers-Fulbright J L,Kita H,et al.A remote sensing image segmentation method based on spectral and texture information fusion[C].International Conference on Information Technology:New Generations,2009.
[15]Wu X D,Kumar V,Quinlan J R,et al.Top 10 algorithms in data mining[J].Knowledge and Information Systems,2008,14(1):1-37.
[16]张剑清,段艳.一种结合分水岭与决策树C5.0的极化SAR分类方法[J].武汉大学学报·信息科学版,2014,39(8):891-896.[Zhang J Q,Duan Y.A supervised classification method of polarimetric sythetic aperture radar data using watershed segmentation and decision tree C5.0[J].Journal of Geo-Information Science,2014,39(8):891-896.]
[17]李海江,刘耀林,谭荣辉,等.一种新的基于高分辨率全色影像的城市建成区边界提取算法[J].测绘通报,2015(7):36-41.[Li H J,Liu Y L,Tan R H,et al.A new algorithm of urban built-up area boundary extraction based on highresolution panchromatic image[J].Bulletin of Surveying and Mapping,2015(7):36-41.]
[18]范作江.遥感与地理信息系统相结合的城市扩展研究——以北京市十年城市扩展为例[D].北京:北京大学,1996.[Fan Z J.Study of urban expansion based on remote sensing and geography information system[D].Beijing:Beijing University,1996.]
[19]魏斌.基于GIS和RS的城市空间扩展研究分析[D].烟台:鲁东大学,2014.[Wei B.Extended research and analysis of the GIS and RS based on city space:A case of study of Qingdao[D].Yantai:Ludong University,2014.]
[20]刘纯,洪亮,周光海.基于TM影像的衡阳市城区扩展趋势研究[J].昆明冶金高等专科学校学报,2013,29(5):15-19.[Liu C,Hong L,Zhou G H.A study on urban sprawl in Hengyang city based on TM image[J].Journal of Kunming Metallurgy College,2013,29(5):15-19.]