测绘通报 ›› 2018, Vol. 0 ›› Issue (11): 86-89.doi: 10.13474/j.cnki.11-2246.2018.0356

• 技术交流 • 上一篇    下一篇

地形熵聚类下利用多面函数拟合高程异常

孙佳龙1,2,3,4, 郭淑艳5, 龙冰心1, 秦思远1   

  1. 1. 淮海工学院测绘与海洋信息学院, 江苏 连云港 222005;
    2. 海岛(礁)测绘技术国家测绘地理信息局重点实验室, 山东 青岛 266590;
    3. 海岸带地理环境监测国家测绘地理信息局重点实验室, 广东 深圳 518060;
    4. 江苏省海洋资源开发研究院, 江苏 连云港 222001;
    5. 北京大地宏图勘测科技有限公司, 北京 100029
  • 收稿日期:2018-02-22 修回日期:2018-04-13 出版日期:2018-11-25 发布日期:2018-11-29
  • 作者简介:孙佳龙(1977-),男,博士,副教授,主要研究方向为海洋大地测量。E-mail:jialongsun@126.com
  • 基金资助:
    国家自然科学基金(40974016);江苏省海洋科学技术优势学科建设项目;海岛(礁)测绘技术国家测绘地理信息局重点实验室基金(2018B09)

A Polyhedral Function Fitting Method of Elevation Anomaly with Terrain Entropy and Clustering

SUN Jialong1,2,3,4, GUO Shuyan5, LONG Bingxin1, QIN Siyuan1   

  1. 1. School of Geomatics and Marine Information, Huaihai Institute of Technology, Lianyungang 222005, China;
    2. Key Laboratory of Surveying and Mapping Technology on Island and Reef of SBSM, Qingdao 266590, China;
    3. Key Laboratory of Geo-Environmental Monitoring of Coastal Zone of SBSM, Shenzhen 518060, China;
    4. Jiangsu Marine Resource Development Research Insititute, Lianyungang 222001, China;
    5. Beijing Geodetic Survey Technology Co., Ltd., Beijing 100029, China
  • Received:2018-02-22 Revised:2018-04-13 Online:2018-11-25 Published:2018-11-29

摘要: 提出了利用地形熵确定高程异常点分类数的方法,以距离与地形熵的比值作为指标,从高程异常点中选择中心点,利用多面函数拟合了高程异常曲面。结果显示,利用地形熵聚类拟合的高程异常,比单独采用聚类分析和地形熵的方法在外符合精度上分别提高了28%和48%。

关键词: 高程异常, 多面函数, 地形熵, 聚类分析

Abstract: A method is put forward to determine the classification number of elevation anomaly with terrain entropy.The ratio of distance and terrain entropy is taken as an index to select the center points of polyhedral function.On this basis,height anomaly surface is fitted with the selected center points.In order to compare the advantages and disadvantages of various methods,the outside precision is taken as an index to evaluate the accuracy of various methods.The results show that the accuracy of surface fitting using terrain entropy and clustering is 28% higher than that using clustering analysis alone,is 48% higher than that using terrain entropy alone.

Key words: elevation anomaly, polyhedral function, terrain entropy, cluster analysis

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