A Line Simplification Method Based on Support Vector Machine

DUAN Peixiang; QIAN Haizhong; HE Haiwei; XIE Limin; LUO Denghan

doi:10.13203/j.whugis20180434

Volume 45 Issue 5

May 2020

Turn off MathJax

Article Contents

Abstract

References

Geomatics and Information Science of Wuhan University > 2020 > 45(5): 744-752, 783. > DOI: 10.13203/j.whugis20180434

DUAN Peixiang, QIAN Haizhong, HE Haiwei, XIE Limin, LUO Denghan. A Line Simplification Method Based on Support Vector Machine[J]. Geomatics and Information Science of Wuhan University, 2020, 45(5): 744-752, 783. DOI: 10.13203/j.whugis20180434

Citation:

PDF (3982 KB)

A Line Simplification Method Based on Support Vector Machine

1.
Institute of Geospatial Information, Information Engineering University, Zhengzhou 450001, China
2.
32176 Troops, Kuerle 841000, China

Funds:

The National Natural Science Foundation of China 41571442

The National Natural Science Foundation of China 41171305

More Information

Author Bio:
DUAN Peixiang, master, specializes in map automatic generalization and spatial data automatic updating. E ‐ mail:1515461929@qq.com
Received Date: September 25, 2019
Published Date: May 04, 2020

Graphical Abstract

Abstract

Abstract

Line simplification is one of the most important parts of automatic map generalization. Aiming at the problem that the effect of the current line simplification methods is too dependent on the artificial setting of algorithm parameters and thresholds, and lacking the adaptive learning ability for different simplification environments, this paper considers the line simplification as a binary classification problem about the selection or deletion of the simplification unit. From the perspective of case-based studying, a new line simplification method based on support vector machine (SVM) is proposed. Firstly, point and bend are taken as the simplification unit, the simplification case is automatically obtained from the simplification results of experts, and then the feature description items are extracted as the attribute space of the case sample. The line simplification classifier is trained by SVM. Finally, the SVM classifier is used to classify the simplification units of the new line elements into selection and deletion, so that the line simplification is realized. The experimental results show that this method can well reduce the simplification intention of the experts by learning the simplification case, with a high classification accuracy for the selection and deletion of simplification units, and can adaptively complete the line simplification.
- automatic map generalization,
- line simplification,
- case-based studying,
- support vector machine

FullText(HTML)

References (22)

References

[1]	郭庆胜.地图自动综合理论与方法[M].北京:测绘出版社, 2002 Guo Qingsheng. Theory and Method of Automatic Map Generalization[M]. Beijing:Surveying and Mapping Press, 2002
[2]	王家耀.普通地图制图综合原理[M].北京:测绘出版社, 1993 Wang Jiayao. Principles of Cartographic Generalization of Map[M]. Beijing:Surveying and Mapping Press, 1993
[3]	Douglas D H, Peucker T K. Algorithms for the Reduction of Points Required to Represent a Digitized Line or Its Caricature[J]. Canadian Cartographer, 1993, 10:112-122 http://d.old.wanfangdata.com.cn/Periodical/jsjyy2009z2100
[4]	Li Zhilin, Openshaw S. Algorithms for Objective Generalization of Line Features Based on the Natural Principle[J]. International Journal of Geographical Information Systems, 1992, 6(5):373-389 doi: 10.1080/02693799208901921
[5]	Nakos B, Mitropoulos V. Local Length Ratio as a Measure of Critical Point Detection for Line Simplification[R]. Symposium of the 5th ICA Workshop on Progress in Automated Map Generalization, Paris, France, 2003
[6]	Teh C H, Chin R T. On the Detection of Dominant Points on Digital Curves[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 11(8):859-872 http://d.old.wanfangdata.com.cn/OAPaper/oai_doaj-articles_e0d12ffff31416a510cb41b0aeec8e29
[7]	Chrobak T. A Numerical Method for Generalizing the Linear Elements of Large-Scale Maps, Based on the Example of Rivers[J]. Cartographica:The International Journal for Geographic Information & Geovisualization, 2000, 37(1):49-56 https://www.researchgate.net/publication/270056786_A_Numerical_Method_for_Generalizing_the_Linear_Elements_of_Large-Scale_Maps_Based_on_the_Example_of_Rivers
[8]	朱鲲鹏, 武芳, 王辉连, 等. Li-Openshaw算法的改进与评价[J].测绘学报, 2007, 36(4):450-456 doi: 10.3321/j.issn:1001-1595.2007.04.015 Zhu Kunpeng, Wu Fang, Wang Huilian, et al. Improvement and Assessment of Li-Openshaw Algorithm[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(4):450-456 doi: 10.3321/j.issn:1001-1595.2007.04.015
[9]	刘慧敏, 樊子德, 徐震, 等.曲线化简的弧比弦算法改进及其评价[J].地理与地理信息科学, 2011, 27(1):45-48 http://d.old.wanfangdata.com.cn/Periodical/dlxygtyj201101011 Liu Huimin, Fan Zide, Xu Zhen, et al. An Improved Local Length Ratio Method for Curve Simplification and Its Evaluation[J]. Geography and Geo-information Science, 2011, 27(1):45-48 http://d.old.wanfangdata.com.cn/Periodical/dlxygtyj201101011
[10]	邓敏, 陈杰, 李志林, 等.曲线简化中节点重要性度量方法比较及垂比弦法的改进[J].地理与地理信息科学, 2009, 25(1):40-43 http://d.old.wanfangdata.com.cn/Periodical/dlxygtyj200901011 Deng Min, Chen Jie, Li Zhilin, et al. An Improved Local Measure Method for the Importance of Vertices in Curve Simplification[J]. Geography and Geo-information Science, 2009, 25(1):40-43 http://d.old.wanfangdata.com.cn/Periodical/dlxygtyj200901011
[11]	张青年, 廖克.基于结构分析的曲线概括方法[J].中山大学学报(自然科学版), 2001, 40(5):118-121 doi: 10.3321/j.issn:0529-6579.2001.05.030 Zhang Qingnian, Liao Ke. Line Generalization Based on Structure Analysis[J]. Acta Scientiarum Naturalium Universitatis Sunyatseni, 2001, 40(5):118-121 doi: 10.3321/j.issn:0529-6579.2001.05.030
[12]	钱海忠, 武芳, 陈波, 等.采用斜拉式弯曲划分的曲线化简方法[J].测绘学报, 2007, 36(4):443-449, 456 doi: 10.3321/j.issn:1001-1595.2007.04.014 Qian Haizhong, Wu Fang, Chen Bo, et al. Simplifying Line with Oblique Dividing Curve Method[J]. Acta Geodaetica et Cartographica Sinica, 2007, 36(4):443-449, 456 doi: 10.3321/j.issn:1001-1595.2007.04.014
[13]	黄博华, 武芳, 翟仁健, 等.保持弯曲特征的线要素化简算法[J].测绘科学技术学报, 2014, 31(5):533-537 doi: 10.3969/j.issn.1673-6338.2014.05.020 Huang Bohua, Wu Fang, Zhai Renjian, et al. The Line Feature Simplification Algorithm Preserving Curve Bend Feature[J]. Journal of Geomatics Science and Technology, 2014, 31(5):533-537 doi: 10.3969/j.issn.1673-6338.2014.05.020
[14]	钱海忠, 何海威, 王骁, 等.采用三元弯曲组划分的线要素化简方法[J].武汉大学学报·信息科学版, 2017, 42(8):1096-1103 http://ch.whu.edu.cn/CN/abstract/abstract5802.shtml Qian Haizhong, He Haiwei, Wang Xiao, et al. Line Feature Simplification Method Based on Bend Group Division[J]. Geomatics and Information Science of Wuhan University, 2017, 42(8):1096-1103 http://ch.whu.edu.cn/CN/abstract/abstract5802.shtml
[15]	武芳, 邓红艳.基于遗传算法的线要素自动化简模型[J].测绘学报, 2003, 32(4):349-355 doi: 10.3321/j.issn:1001-1595.2003.04.013 Wu Fang, Deng Hongyan. Using Genetic Algorithm for Solving Problems in Automated Line Simplification[J]. Acta Geodaetica et Cartographica Sinica, 2003, 32(4):349-355 doi: 10.3321/j.issn:1001-1595.2003.04.013
[16]	Jiang B, Nakos B.Line Simplification Using Self-Organizing Maps[R].ISPRS Workshop on Spatial Analysis and Decision Making, Hong Kong, China, 2003
[17]	钱海忠, 武芳, 王家耀.自动制图综合及其过程控制的智能化研究[M].北京:测绘出版社, 2012 Qian Haizhong, Wu Fang, Wang Jiayao. Study of Automated Cartographic Generalization and Intelligentized Generalization Process Control[M]. Beijing:Surveying and Mapping Press, 2012
[18]	何海威, 钱海忠, 刘闯, 等.案例推理的制图综合应用背景与方法[J].测绘科学技术学报, 2017, 34(4):427-432 http://d.old.wanfangdata.com.cn/Periodical/chxyxb201704018 He Haiwei, Qian Haizhong, Liu Chuang, et al. Application Background and Method of Case Based Reasoning in Cartographic Generalization[J]. Journal of Geomatics Science and Technology, 2017, 34(4):427-432 http://d.old.wanfangdata.com.cn/Periodical/chxyxb201704018
[19]	毋河海.数字曲线拐点的自动确定[J].武汉大学学报·信息科学版, 2003, 28(3):330-335 http://ch.whu.edu.cn/CN/abstract/abstract4830.shtml Wu Hehai. Automatic Determination of Inflection Point and Its Application[J]. Geomatics and Information Science of Wuhan University, 2003, 28(3):330-335 http://ch.whu.edu.cn/CN/abstract/abstract4830.shtml
[20]	Wang Zeshen, Muller J C. Line Generalization Based on Analysis of Shape Characteristics[J]. Cartography and Geographic Information Systems, 1998, 25(1):3-15 doi: 10.1559/152304098782441750
[21]	郭庆胜, 黄远林, 章莉萍.曲线的弯曲识别方法研究[J].武汉大学学报·信息科学版, 2008, 33(6):596-599 http://ch.whu.edu.cn/CN/abstract/abstract1620.shtml Guo Qingsheng, Huang Yuanlin, Zhang Liping. The Method of Curve Bend Recognition[J]. Geomatics and Information Science of Wuhan University, 2008, 33(6):596-599 http://ch.whu.edu.cn/CN/abstract/abstract1620.shtml
[22]	武芳, 朱鲲鹏.线要素化简算法几何精度评估[J].武汉大学学报·信息科学版, 2008, 33(6):600-603 http://ch.whu.edu.cn/CN/abstract/abstract1621.shtml Wu Fang, Zhu Kunpeng. Geometric Accuracy Assessment of Linear Features' Simplification Algorithms[J]. Geomatics and Information Science of Wuhan University, 2008, 33(6):600-603 http://ch.whu.edu.cn/CN/abstract/abstract1621.shtml

[1]	XU Shenghua, LIU Jiping, WANG Xianghong, ZHANG Yu, LIN Rongfu, ZHANG Meng, LIU Mengmeng, JIANG Tao. Landslide Susceptibility Assessment Method Incorporating Index of Entropy Based on Support Vector Machine: A Case Study of Shaanxi Province[J]. Geomatics and Information Science of Wuhan University, 2020, 45(8): 1214-1222. DOI: 10.13203/j.whugis20200109
[2]	TIAN Wenzhe, FU Randi, JIN Wei, LIU Zhen, YIN Caoqian. Adaptive Fuzzy Support Vector Machine for Classification of Clouds in Satellite Imagery[J]. Geomatics and Information Science of Wuhan University, 2017, 42(4): 488-495. DOI: 10.13203/j.whugis20140734
[3]	WU Xueling, REN Fu, NIU Ruiqing, PENG Ling. Landslide Spatial Prediction Based on SlopeUnits and Support Vector Machines[J]. Geomatics and Information Science of Wuhan University, 2013, 38(12): 1499-1503.
[4]	WU Zhaocong, OUYANG Qundong, HU Zhongwen. Polarimetric SAR Image Classification Using Watershed-Transformation and Support Vector Machine[J]. Geomatics and Information Science of Wuhan University, 2012, 37(1): 7-10.
[5]	FAN Qian, HUA Xianghong. A Novel Method for Forecasting Landslide Displacement Based on Phase Space Reconstruction and Support Vector Machine[J]. Geomatics and Information Science of Wuhan University, 2009, 34(2): 248-251.
[6]	DENG Xingsheng, HUA Xianghong. Learning Algorithm of Dynamic Least Square Support Vector Machine[J]. Geomatics and Information Science of Wuhan University, 2008, 33(11): 1122-1125.
[7]	WANG Xinzhou, FAN Qian, XU Chengquan, LI Zhao. Dam Deformation Prediction Based on Wavelet Transform and Support Vector Machine[J]. Geomatics and Information Science of Wuhan University, 2008, 33(5): 469-471.
[8]	XU Fang, MEI Wensheng, ZHANG Zhihua. Least Squares Support Vector Machines for Aerial Images Segmentation[J]. Geomatics and Information Science of Wuhan University, 2005, 30(8): 694-698.
[9]	MEI Jianxin, DUAN Shan, QIN Qianqing. Method for Special Targets Detection Based on Support Ector Machines[J]. Geomatics and Information Science of Wuhan University, 2004, 29(10): 912-915,932.
[10]	XU Fang, YAN Qin. Texture Classification of Aerial Images Based on Support Vector Machines[J]. Geomatics and Information Science of Wuhan University, 2003, 28(5): 517-520.

Cited By

Cited by

Periodical cited type(3)

1.	石伟，胡玮，冯雪东，贾昊东. 乌加河地震台JCZ-1地震计与PET重力仪地震波记录特征分析. 地震地磁观测与研究. 2023(03): 133-138 .
2.	李航，陈晓东，徐建桥，孙和平，张苗苗，韩宇飞. 同址观测重力仪与地震仪地震频段噪声水平比较. 大地测量与地球动力学. 2020(08): 843-848 .
3.	田桂娥，张建. 基于武汉超导数据的非潮汐信息信号提取方法研究. 河北师范大学学报(自然科学版). 2019(06): 497-503 .

Other cited types(2)

Get Citation

PDF

XML

Article views (1587) PDF downloads (100) Cited by(5)

A Line Simplification Method Based on Support Vector Machine

Abstract

References

Related Articles

Cited by

Periodical cited type(3)

Other cited types(2)

Catalog

Related

A Line Simplification Method Based on Support Vector Machine

Abstract

References

Related Articles

Cited by

Periodical cited type(3)

Other cited types(2)

Catalog

Related

Export File

Citation

Format

Content