基于小波分析的复杂地貌区DEM自动综合研究
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摘要
地理空间数据的自动综合是一个国际性难题,已有方法难以实现智能化"取主舍次"的制图综合原则。该研究基于小波多分辨率分析原理,以黄土高原样区不同复杂度地貌5 m分辨率DEM为研究对象,通过改变自动综合过程中水平、垂直与对角方向的地貌高频信息,得到一系列不同尺度、不同详细程度的DEM,实现了复杂地貌区域DEM的自动综合。结果表明:在遵循地图综合基本原则的基础上,该方法可对地貌主次信息区别对待,通过保留不同高频信息,可不同程度凸显地貌的局部细节特征,而地貌的基本骨架保持不变;且对于不同复杂度地貌,保留相同高频信息,细节地貌特征的综合程度亦有所不同,其地貌依赖特性对于复杂地貌的自动综合有明显优势。
Automated synthesis of geospatial data is a great challenge worldwide.The previously existing methods were not able to realize the principle of intelligent cartographic generalization.In this study,a DEM progressive automatic generalization model was constructed based on the idea of wavelet multi-resolution analysis,and applied to the 5 m resolution DEM with different complexity of sample regions in the Loess Plateau area.By adding high-frequency details along the horizontal,vertical and diagonal directions,we automatically synthesized a series of DEM with different scales and different degrees of details and achieved a progressive multi-scale expression of DEM.The results show that,based on the basic principles of map synthesis,this method can be used to distinguish the primary and secondary information.The local details of the geomorphologic features are highlighted by retaining a range of high-frequency information,while the basic skeleton of the landform remains unchanged.Moreover,as for the different complexity of the landform,when retaining the same high-frequency information,the degree of details of the geomorphologic features is also different.In other words,the degree of automatic synthesis depends on the geomorphologic form.The geomorphologic dependence of the method has demonstrated its advantages in the automatic synthesis of complex landform.
Automated synthesis of geospatial data is a great challenge worldwide.The previously existing methods were not able to realize the principle of intelligent cartographic generalization.In this study,a DEM progressive automatic generalization model was constructed based on the idea of wavelet multi-resolution analysis,and applied to the 5 m resolution DEM with different complexity of sample regions in the Loess Plateau area.By adding high-frequency details along the horizontal,vertical and diagonal directions,we automatically synthesized a series of DEM with different scales and different degrees of details and achieved a progressive multi-scale expression of DEM.The results show that,based on the basic principles of map synthesis,this method can be used to distinguish the primary and secondary information.The local details of the geomorphologic features are highlighted by retaining a range of high-frequency information,while the basic skeleton of the landform remains unchanged.Moreover,as for the different complexity of the landform,when retaining the same high-frequency information,the degree of details of the geomorphologic features is also different.In other words,the degree of automatic synthesis depends on the geomorphologic form.The geomorphologic dependence of the method has demonstrated its advantages in the automatic synthesis of complex landform.
引文
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[2] 汤国安.我国数字高程模型与数字地形分析研究进展[J].地理学报,2014,69(9):1305-1325.
[3] 程德强,高春留,赵牡丹,等.基于DEM与遥感影像数据的梯田信息强化表达研究[J].干旱区资源与环境,2016,30(11):124-130.
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[11] 刘民士,龙毅,费立凡.地貌与水系自动综合研究综述[J].地理与地理信息科学,2015,31(5):48-52.
[12] 呼雪梅,秦承志.地形信息对确定DEM适宜分辨率的影响[J].地理科学进展,2014,33(1):50-56.
[13] 高超,金高洁.SWIM水文模型的DEM尺度效应[J].地理研究,2012,31(3):399-408.
[14] 刘学军,王彦芳,晋蓓,等.顾及数据特性的格网DEM分辨率计算[J].地理研究,2010,29(5):852-862.
[15] 秦承志,呼雪梅.栅格数字地形分析中的尺度问题研究方法[J].地理研究,2014,33(2):270-283.
[16] 杨昕,汤国安,刘学军,等.数字地形分析的理论、方法与应用[J].地理学报,2009,64(9):1058-1070.
[17] 黄骁力,汤国安,刘凯.DEM分辨率对地形纹理特征提取的影响[J].地球信息科学学报,2015,17(7):822-829.
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[20] LI Z L,OPENSHAW S.Algorithms for automated line generalization1 based on a natural principle of objective generalization[J].International Journal of Geographical Information Systems,1992,6(5):373-389.
[21] 禄小敏,闫浩文,王中辉,等.顾及权重的点群目标自动综合算法[J].测绘科学,2018,43(12):85-91.
[22] 张青年,秦建新.面状分布地物群识别与概括的数学形态学方法[J].地理研究,2000,19(1):93-100.
[23] 吕一河,傅伯杰.生态学中的尺度及尺度转换方法[J].生态学报,2001,1(12):2096-2105.
[24] 李精忠,李冬琳.一种基于汇水区合并的DEM综合方法[J].武汉大学学报(信息科学版),2015,40(8):1095-1099.
[25] 潘东华,王静爱,贾慧聪.灾害风险地图中黄土高原地貌自动综合研究[J].测绘通报,2015,61(7):58-61.
[26] 刘春,王家林,刘大杰.多尺度小波分析用于DEM网格数据综合[J].中国图象图形学报,2004,9(3):340-344.
[27] 李婧晗,江岭,左颖,等.面向对象的安徽省基本地貌类型划分方法[J].地理与地理信息科学,2018,34(5):80-85.
[28] 吴纪桃,王桥.复杂地貌形态多比例尺表达的二维小波分析研究[J].遥感学报,2003,7(2):93-97.
[29] 王明常,王宇,邢立新,等.多分辨率小波分析在线状要素简化中的应用[J].地理与地理信息科学,2005,21(5):38-40.
[30] 陶旸,王春,蒋圣.一种基于改进双树复小波变换的地形纹理特征提取方法[J].地理与地理信息科学,2017,33(4):47-50.
[31] 于浩,杨勤科,张晓萍,等.基于小波多尺度分析的DEM数据综合及尺度转换[J].地理与地理信息科学,2009,25(4):12-16.
[32] 于浩,杨勤科,张晓萍,等.基于小波多尺度分析的DEM数据综合研究[J].测绘科学,2008,33(3):93-95.
[33] 林璐.基于小波分析的多尺度DEM表达及地形分析效应研究——以黄土高原样区为例[D].西安:西北大学,2008.
[34] 李含璞.基于小波变换的DEM多尺度综合研究[D].兰州:兰州大学,2006.
[2] 汤国安.我国数字高程模型与数字地形分析研究进展[J].地理学报,2014,69(9):1305-1325.
[3] 程德强,高春留,赵牡丹,等.基于DEM与遥感影像数据的梯田信息强化表达研究[J].干旱区资源与环境,2016,30(11):124-130.
[4] 李志林.数字地形分析[J].地理学报,2006,61(12):1326.
[5] GOODCHILD M F,EGENHOFER M J,KEMP K K,et al.Introduction to the Varenius project[J].International Journal of Geographical Information Systems,1999,13(8):731-745.
[6] WU F.Multi-scale representation and automatic generalization of relief based on wavelet analysis[J].Editoral Board of Geomatics & Information Science of Wuhan University,2001,26(2):170-176.
[7] KATERJI W,FARJAS A M,BALSERA M D C.DEM local accuracy patterns in land-use/land-cover classification[J].Open Geosciences,2016,8(1):760-770.
[8] 刘学军,卢华兴,仁政,等.论DEM地形分析中的尺度问题[J].地理研究,2007,26(3):433-442.
[9] 汤国安,李发源,熊礼阳.黄土高原数字地形分析研究进展[J].地理与地理信息科学,2017,33(4):1-7.
[10] 汤国安,那嘉明,程维明.我国区域地貌数字地形分析研究进展[J].测绘学报,2017,46(10):1570-1591.
[11] 刘民士,龙毅,费立凡.地貌与水系自动综合研究综述[J].地理与地理信息科学,2015,31(5):48-52.
[12] 呼雪梅,秦承志.地形信息对确定DEM适宜分辨率的影响[J].地理科学进展,2014,33(1):50-56.
[13] 高超,金高洁.SWIM水文模型的DEM尺度效应[J].地理研究,2012,31(3):399-408.
[14] 刘学军,王彦芳,晋蓓,等.顾及数据特性的格网DEM分辨率计算[J].地理研究,2010,29(5):852-862.
[15] 秦承志,呼雪梅.栅格数字地形分析中的尺度问题研究方法[J].地理研究,2014,33(2):270-283.
[16] 杨昕,汤国安,刘学军,等.数字地形分析的理论、方法与应用[J].地理学报,2009,64(9):1058-1070.
[17] 黄骁力,汤国安,刘凯.DEM分辨率对地形纹理特征提取的影响[J].地球信息科学学报,2015,17(7):822-829.
[18] WEIBEL R.Models and experiments for adaptive computer-assisted terrain generalization[J].American Cartographer,2013,19(3):133-153.
[19] LIU C,LIN W J,UNIVERSITY T,et al.Generalization of DEM grid data based on multi band wavelet analysis[J].Journal of Image & Graphics,2004,9(3):340-344.
[20] LI Z L,OPENSHAW S.Algorithms for automated line generalization1 based on a natural principle of objective generalization[J].International Journal of Geographical Information Systems,1992,6(5):373-389.
[21] 禄小敏,闫浩文,王中辉,等.顾及权重的点群目标自动综合算法[J].测绘科学,2018,43(12):85-91.
[22] 张青年,秦建新.面状分布地物群识别与概括的数学形态学方法[J].地理研究,2000,19(1):93-100.
[23] 吕一河,傅伯杰.生态学中的尺度及尺度转换方法[J].生态学报,2001,1(12):2096-2105.
[24] 李精忠,李冬琳.一种基于汇水区合并的DEM综合方法[J].武汉大学学报(信息科学版),2015,40(8):1095-1099.
[25] 潘东华,王静爱,贾慧聪.灾害风险地图中黄土高原地貌自动综合研究[J].测绘通报,2015,61(7):58-61.
[26] 刘春,王家林,刘大杰.多尺度小波分析用于DEM网格数据综合[J].中国图象图形学报,2004,9(3):340-344.
[27] 李婧晗,江岭,左颖,等.面向对象的安徽省基本地貌类型划分方法[J].地理与地理信息科学,2018,34(5):80-85.
[28] 吴纪桃,王桥.复杂地貌形态多比例尺表达的二维小波分析研究[J].遥感学报,2003,7(2):93-97.
[29] 王明常,王宇,邢立新,等.多分辨率小波分析在线状要素简化中的应用[J].地理与地理信息科学,2005,21(5):38-40.
[30] 陶旸,王春,蒋圣.一种基于改进双树复小波变换的地形纹理特征提取方法[J].地理与地理信息科学,2017,33(4):47-50.
[31] 于浩,杨勤科,张晓萍,等.基于小波多尺度分析的DEM数据综合及尺度转换[J].地理与地理信息科学,2009,25(4):12-16.
[32] 于浩,杨勤科,张晓萍,等.基于小波多尺度分析的DEM数据综合研究[J].测绘科学,2008,33(3):93-95.
[33] 林璐.基于小波分析的多尺度DEM表达及地形分析效应研究——以黄土高原样区为例[D].西安:西北大学,2008.
[34] 李含璞.基于小波变换的DEM多尺度综合研究[D].兰州:兰州大学,2006.
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