太阳成8722(中国)有限公司-GREEN NO.1

太阳成8722  >  科学研究  >  科研成果  >  正文
科研成果
博士生汪艳霞的论文在TRANSACTIONS IN GIS刊出
发布时间:2016-02-18 11:06:29     发布者:yz     浏览次数:

标题:A Back-Propagation Neural Network-Based Approach for Multi-Represented Feature Matching in Update Propagation作者:Wang, Yanxia; Chen, Deng; Zhao, Zhiyuan; Ren, Fu; Du, Qingyun

来源出版物:TRANSACTIONS IN GIS 卷:19 期:6 页:964-993 DOI:10.1111/tgis.12138 出版年:DEC 2015

摘要:Spatial data infrastructures, which are characterized by multi-represented datasets, are prevalent throughout the world. The multi-represented datasets contain different representations for identical real-world entities. Therefore, update propagation is useful and required for maintaining multi-represented datasets. The key to update propagation is the detection of identical features in different datasets that represent corresponding real-world entities and the detection of changes in updated datasets. Using polygon features of settlements as examples, this article addresses these key problems and proposes an approach for multi-represented feature matching based on spatial similarity and a back-propagation neural network (BPNN). Although this approach only utilizes the measures of distance, area, direction and length, it dynamically and objectively determines the weight of each measure through intelligent learning; in contrast, traditional approaches determine weight using expertise. Therefore, the weight may be variable in different data contexts but not for different levels of expertise. This approach can be applied not only to one-to-one matching but also to one-to-many and many-to-many matching. Experiments are designed using two different approaches and four datasets that encompass an area in China. The goals are to demonstrate the weight differences in different data contexts and to measure the performance of the BPNN-based feature matching approach.

入藏号:WOS:000368521800008

文献类型:Article

语种:English

扩展关键词:SPATIAL DATA SETS; CONFLATION; DATASETS

通讯作者地址:Du, QY, Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

电子邮件地址:qydu@whu.edu.cn

地址:

[Wang, Yanxia; Chen, Deng; Ren, Fu; Du, Qingyun] Wuhan Univ, Sch Resource & Environm Sci, 129 Luoyu Rd, Wuhan 430079, Peoples R China.

[Zhao, Zhiyuan] Wuhan Univ, State Key Lab Informat Engn, Wuhan, Peoples R China.

研究方向:Geography

ISSN:1361-1682

eISSN: 1467-9671

影响因子(2014):1.398

信息服务
学院网站教师登录 学院办公电话 学校信息门户登录

版权所有 © 太阳成8722
地址:湖北省武汉市珞喻路129号 邮编:430079 
电话:027-68778381,68778284,68778296 传真:027-68778893    邮箱:sres@whu.edu.cn

Baidu
sogou