基于主成分变换模型的DEM格网聚合及其误差分析

doi:10.11947/j.AGCS.2017.20160104

测绘学报 ›› 2017, Vol. 46 ›› Issue (3): 389-397.doi: 10.11947/j.AGCS.2017.20160104

基于主成分变换模型的DEM格网聚合及其误差分析

黄泽纯^1,2, 张倩宁¹, 徐柱^1,2,3, 洪安东¹, 张瑞芳¹

1. 西南交通大学地球科学与环境工程学院, 四川成都 610031;
2. 西南交通大学高速铁路运营安全空间信息技术国家地方联合工程实验室, 四川成都 610031;
3. 西南交通大学“2011计划”轨道交通安全协同创新中心, 四川成都 610031

收稿日期:2016-03-16 修回日期:2016-09-20 出版日期:2017-03-20 发布日期:2017-04-11
作者简介:黄泽纯(1974-),男,博士生,副教授,研究方向为数字地形分析理论与应用。E-mail:zchuang@yeah.net
基金资助:
测绘地理信息公益性行业科研专项（201512028）；教育部长江学者和创新团队发展资助计划（IRT13092）；中央高校基本科研业务费专项资金（2682014CX017）

The DEM Grid Aggregation Based on the Principal Component Transform Model and Its Uncertainty Analysis

HUANG Zechun^1,2, ZHANG Qianning¹, XU Zhu^1,2,3, HONG Andong¹, ZHANG Ruifang¹

1. Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China;
2. State-Province Joint Engineering Laboratory of Spatial Information Technology for High-speed Railway Safety, Southwest Jiaotong University, Chengdu 610031, China;
3. Collaborative Innovation Center for Rail Transport Safety, Ministry of Education of the People's Republic of China, Southwest Jiaotong University, Chengdu 610031, China

Received:2016-03-16 Revised:2016-09-20 Online:2017-03-20 Published:2017-04-11
Supported by:
Public Science Research Program of Surveying, Mapping and Geoinformation (No.201512028);Program for Changjiang Scholars and Innovative Research Team in University (No. IRT13092);Fundamental Research Funds for the Central Universities(No. 2682014CX017)

摘要/Abstract

摘要： 利用主成分分析揭示变量之间关系的特性，进而提出一种既能保证较高精度又能较好地保持地形形态特征的DEM格网聚合方法。首先根据主成分变换模型推导DEM格网聚合数学公式，构建主成分聚合模型；然后以30m分辨率DEM转换为90m分辨率DEM为例，根据格网点属性间的权重关系聚合重构DEM。在此基础上，以均值聚合和双线性重采样聚合方法为比较对象，从聚合前后的检查点高程偏差的统计描述、空间分布与自相关性、地形形态保持程度方面分析3种聚合策略下重构DEM的误差特性。最后运用描述统计、半变异分析和等高线套合方法，定量评价主成分聚合重构DEM的质量效果。试验分析结果表明，同均值聚合和重采样聚合相比较，该方法重构的DEM既能保持较高精度，又能很好地保持地形形态特征。

关键词: 数字高程模型, 主成分分析, 格网聚合, 误差分析, 地形特征

Abstract: The object is to present a new DEM aggregation method which not only can ensure high precision of DEM, but also can maintain the terrain morphology better according to the characteristic of principal component analysis which can reveal the relationship between variables. First of all, the mathematical formula deduction and practical calculation procedures about the new DEM grid aggregation method are presented on the basis of principal component transformation model. The principal component aggregation method is applied to get the weights of DEM grid cells in the 3×3 filter window to rebuild new DEM. Then taking converting DEM with 30 m resolution into the DEM with 90 m resolution as an example, three new DEMs are rebuilt respectively using principal component aggregation, mean aggregation and bilinear resample aggregation.Based on the model, the uncertainty characteristic of the DEM rebuilt with three aggregation methods are analyzed from elevation deviation before and after grid aggregation, the spatial distribution and spatial autocorrelation, and the keeping level of the terrain feature. Finally, the quality of DEM rebuilt with principal component aggregation is evaluated with descriptive statistics, semi variance function and contour overlay method.Experimental analysis results show that the new method can maintain the terrain feature better under keeping the high precision of DEM compared with mean aggregation and bilinear resampling aggregation methods.

Key words: DEM, principal component analysis, grid aggregation, uncertainty analysis, terrain feature

中图分类号:

P207

黄泽纯, 张倩宁, 徐柱, 洪安东, 张瑞芳. 基于主成分变换模型的DEM格网聚合及其误差分析[J]. 测绘学报, 2017, 46(3): 389-397.

HUANG Zechun, ZHANG Qianning, XU Zhu, HONG Andong, ZHANG Ruifang. The DEM Grid Aggregation Based on the Principal Component Transform Model and Its Uncertainty Analysis[J]. Acta Geodaetica et Cartographica Sinica, 2017, 46(3): 389-397.

参考文献

[1] LI Zhilin,ZHU Qing, GOLD C. Digital Terrain Modeling:Principles and Methodology[M]. Boca Raton, FL:CRC Press, 2004:1-7.
[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. TANG Guo'an. Progress of DEM and Digital Terrain Analysis in China[J]. Acta Geographica Sinica, 2014, 69(9):1305-1325.
[3] 卢丽君, 张继贤, 王腾. 一种基于高分辨率雷达影像以及外部DEM辅助的复杂地形制图方法[J]. 测绘学报, 2011, 40(4):459-463. LU Lijun, ZHANG Jixian, WANG Teng. A DEM Mapping Method Assisted by External DEM with High Resolution InSAR Data in Complex Terrain Area[J]. Acta Geodaetica et Cartographica Sinica, 2011, 40(4):459-463.
[4] 云烨, 曾琪明, 焦健, 等. 基于参考DEM的机载InSAR定标方法[J]. 测绘学报, 2014, 43(1):74-82. DOI:10.13485/j.cnki.11-2089.2014.0011. YUN Ye, ZENG Qiming, JIAO Jian, et al. Calibration of Airborne Interferometric SAR Data Based on Reference DEM[J]. Acta Geodaetica et Cartographica Sinica, 2014, 43(1):74-82. DOI:10.13485/j.cnki.11-2089.2014.0011.
[5] 白燕, 廖顺宝, 孙九林. 栅格化属性精度损失的评估方法及其尺度效应分析——以四川省1:25万土地覆被数据为例[J]. 地理学报, 2011, 66(5):709-717. BAI Yan, LIAO Shunbao, SUN Jiulin. Evaluating Methods and Scale Effects of Attribute Information Loss in Rasterization:A Case Study of 1:250000 Land Cover Data of Sichuan[J]. Acta Geographica Sinica, 2011, 66(5):709-717.
[6] 陈永刚, 汤国安, 祝士杰. DEM重采样误差空间分布格局及差异性分析[J]. 中国矿业大学学报, 2011, 40(4):653-659. CHEN Yonggang, TANG Guo'an, ZHU Shijie. Spatial Point Pattern of DEM Resampling Error and Difference Analysis of Influence Factor[J].Journal of China University of Mining & Technology, 2011, 40(4):653-659.
[7] 卢华兴, 刘学军, 王永君, 等. 插值条件下格网DEM坡度计算模型的噪声误差分析[J]. 测绘学报, 2012, 41(6):926-932. LU Huaxing, LIU Xuejun, WANG Yongjun, et al. Noise Error Analysis of Slope Algorithms Based on Grid DEM Derived from Interpolation[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(6):926-932.
[8] 顾绍红, 王永生, 王光霞. 主成分分析模型在数据处理中的应用[J]. 测绘科学技术学报, 2007, 24(5):387-390. GU Shaohong, WANG Yongsheng, WANG Guangxia. Application of Principal Component Analysis Model in Data Processing[J]. Journal of Zhengzhou Institute of Surveying and Mapping, 2007, 24(5):387-390.
[9] ABDI H, WILLIAMS L J. Principal Component Analysis[J]. Wiley Interdisciplinary Reviews:Computational Statistics, 2010, 2(4):433-459.
[10] 赵丽红, 孙宇舸, 蔡玉, 等. 基于核主成分分析的人脸识别[J]. 东北大学学报(自然科学版), 2006, 27(8):847-850. ZHAO Lihong, SUN Yuge, CAI Yu, et al. Face Recognition Based on Kernel PCA[J]. Journal of Northeastern University (Natural Science), 2006, 27(8):847-850.
[11] 杨红磊, 彭军还, 李淑慧, 等. 基于对数-主成分变换的EM算法用于遥感影像分类[J]. 测绘学报, 2010, 39(4):378-382, 403. YANG Honglei, PENG Junhuan, LI Shuhui, et al. Log-principal Component Transformation Based EM Algorithm for Remote Sensing Classification[J]. Acta Geodaetica et Cartographica Sinica, 2010, 39(4):378-382, 403.
[12] 王文波, 赵攀, 张晓东. 利用经验模态分解和主成分分析的SAR图像相干斑抑制[J]. 测绘学报, 2012, 41(6):838-843. WANG Wenbo, ZHAO Pan,ZHANG Xiaodong. Research on SAR Image Speckle Reduction Using EMD and Principle Component Analysis[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(6):838-843.
[13] 宦若虹, 杨汝良. 一种基于特征分类辨识的SAR图像目标检测方法[J]. 测绘学报, 2009, 38(4):324-329. HUAN Ruohong, YANG Ruliang. A Target Detection Method for SAR Image Based on Feature Classification Discrimination[J]. Acta Geodaetica et Cartographica Sinica, 2009, 38(4):324-329.
[14] AGUILAR F J, AGUILAR M A, AGVERA F, et al. The Accuracy of Grid Digital Elevation Models Linearly Constructed from Scattered Sample Data[J]. International Journal of Geographical Information Science, 2006, 20(2):169-192.
[15] OKSANEN J,SARJAKOSKI T.Uncovering the Statistical and Spatial Characteristics of Fine Toposcale DEM Error[J]. International Journal of Geographical Information Science, 2006, 20(4):345-369.
[16] 吴艳兰, 胡海, 胡鹏, 等. 数字高程模型误差及其评价的问题综述[J]. 武汉大学学报:信息科学版, 2011, 36(5):568-574. WU Yanlan, HU Hai, HU Peng, et al. A Review on the Issues in DEM Error and DEM Quality Assessment[J]. Geomatics and Information Science of Wuhan University, 2011, 36(5):568-574.
[17] ÖSTMAN A. Accuracy Estimation of Digital Elevation Data Banks[J]. Photogrammetric Engineering and Remote Sensing, 1987, 53(4):425-430.
[18] LI Zhilin. A Comparative Study of the Accuracy of Digital Terrain Models (DTMS) Based on Various Data Models[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 1994, 49(1):2-11.
[19] WISE S. Assessing the Quality for Hydrological Applications of Digital Elevation Models Derived from Contours[J]. Hydrological Processes, 2000, 14(11-12):1909-1929.
[20] HOLMES K W, CHADWICK O A, KYRIAKIDIS P C. Error in a USGS 30-meter Digital Elevation Model and Its Impact on Terrain Modeling[J]. Journal of Hydrology, 2000, 233(1-4):154-173.
[21] BONIN O, ROUSSEAUX F. Digital Terrain Model Computation from Contour Lines:How to Derive Quality Information from Artifact Analysis[J]. Geoinformatica, 2005, 9(3):253-268.
[22] LÓPEZ C. Improving the Elevation Accuracy of Digital Elevation Models:A Comparison of Some Error Detection Procedures[J]. Transactions in GIS, 2000, 4(1):43-64.
[23] CARLISLE B H. Modelling the Spatial Distribution of DEM Error[J]. Transactions in GIS, 2005,9(4):521-540.
[24] KYRIAKIDIS P C, SHORTRIDGE A M, GOODCHILD M F.Geostatistics for Conflation and Accuracy Assessment of Digital Elevation Models[J]. International Journal of Geographical Information Science, 1999, 13(7):677-707.
[25] SAN B T, SUZEN M L. Digital Elevation Model (DEM) Generation and Accuracy Assessment from Aster Stereo Data[J]. International Journal of Remote Sensing, 2005, 26(22):5013-5027.
[26] 刘学军, 王永君, 龚健雅, 等. DEM流域网络提取算法的误差特性分析[J]. 测绘学报, 2007, 36(2):224-230. LIU Xuejun, WANG Yongjun, GONG Jianya, et al. Error Analysis of Drainage Network Algorithms Based on Digital Elevation Model (DEM)[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(2):224-230.
[27] 卢华兴, 刘学军, 晋蓓. 基于随机过程的格网DEM精度场模型[J]. 测绘学报, 2012, 41(2):273-277. LU Huaxing, LIU Xuejun, JIN Bei. Stochastic Process Based Accuracy Field Model for Grid DEM[J]. Acta Geodaetica et Cartographica Sinica, 2012, 41(2):273-277.
[28] 董有福, 汤国安. 利用地形信息强度进行DEM地形简化研究[J]. 武汉大学学报(信息科学版), 2013, 38(3):353-357. DONG Youfu, TANG Guo'an. Research on Terrain Simplification Using Terrain Significance Information Index from Digital Elevation Models[J]. Geomatics and Information Science of Wuhan University, 2013, 38(3):353-357.

基于主成分变换模型的DEM格网聚合及其误差分析

The DEM Grid Aggregation Based on the Principal Component Transform Model and Its Uncertainty Analysis

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